diff --git a/.gitignore b/.gitignore
index 28f61ff04..9d3615a89 100644
--- a/.gitignore
+++ b/.gitignore
@@ -33,12 +33,5 @@ build/
 # OSX artifacts
 .DS_Store
 
-# Demo artifacts
-demo/data
-demo/models
-demo/store
-demo/media
-demo/reports
-
 # VS Code local configs.
 .vscode/
diff --git a/Makefile b/Makefile
index 9f5f4560e..693beb3e6 100644
--- a/Makefile
+++ b/Makefile
@@ -9,7 +9,7 @@ isort:
 	poetry run isort mlte/
 	poetry run isort test/
 	poetry run isort testbed/
-	poetry run isort demo/*.py
+	poetry run isort demo/
 	poetry run isort tools/
 
 .PHONY: check-isort
@@ -17,7 +17,7 @@ check-isort:
 	poetry run isort --check mlte/
 	poetry run isort --check test/
 	poetry run isort --check testbed/
-	poetry run isort --check demo/*.py
+	poetry run isort --check demo/
 	poetry run isort --check tools/
 
 # Format all source code
@@ -26,8 +26,9 @@ format:
 	poetry run black mlte/
 	poetry run black test/
 	poetry run black testbed/
-	poetry run black demo/*.py
-	poetry run black demo/*.ipynb
+	poetry run black demo/
+	poetry run black demo/simple/*.ipynb
+	poetry run black demo/scenarios/*.ipynb
 	poetry run black tools/
 
 .PHONY: check-format 
@@ -35,8 +36,9 @@ check-format:
 	poetry run black --check mlte/
 	poetry run black --check test/
 	poetry run black --check testbed/
-	poetry run black --check demo/*.py
-	poetry run black --check demo/*.ipynb
+	poetry run black --check demo/
+	poetry run black --check demo/simple/*.ipynb
+	poetry run black --check demo/scenarios/*.ipynb
 	poetry run black --check tools/
 
 # Lint all source code
diff --git a/demo/.gitignore b/demo/.gitignore
new file mode 100644
index 000000000..939a95923
--- /dev/null
+++ b/demo/.gitignore
@@ -0,0 +1,6 @@
+# Demo artifacts
+simple/data
+simple/models
+**/store
+**/media
+**/reports
diff --git a/demo/scenarios/data/FlowerModelv1_TestSetResults.csv b/demo/scenarios/data/FlowerModelv1_TestSetResults.csv
new file mode 100644
index 000000000..71b3027bd
--- /dev/null
+++ b/demo/scenarios/data/FlowerModelv1_TestSetResults.csv
@@ -0,0 +1,6150 @@
+,label,prediced_label,model correct
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diff --git a/demo/scenarios/data/FlowerModelv1_TestSetResults_blur0x8.csv b/demo/scenarios/data/FlowerModelv1_TestSetResults_blur0x8.csv
new file mode 100644
index 000000000..986de4f7b
--- /dev/null
+++ b/demo/scenarios/data/FlowerModelv1_TestSetResults_blur0x8.csv
@@ -0,0 +1,6150 @@
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diff --git a/demo/scenarios/data/FlowerModelv1_TestSetResults_blur2x8.csv b/demo/scenarios/data/FlowerModelv1_TestSetResults_blur2x8.csv
new file mode 100644
index 000000000..5cecddb22
--- /dev/null
+++ b/demo/scenarios/data/FlowerModelv1_TestSetResults_blur2x8.csv
@@ -0,0 +1,6150 @@
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diff --git a/demo/scenarios/data/FlowerModelv1_TestSetResults_blur5x8.csv b/demo/scenarios/data/FlowerModelv1_TestSetResults_blur5x8.csv
new file mode 100644
index 000000000..cd7b68bcc
--- /dev/null
+++ b/demo/scenarios/data/FlowerModelv1_TestSetResults_blur5x8.csv
@@ -0,0 +1,6150 @@
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diff --git a/demo/scenarios/data/FlowerModelv1_TestSetResults_noB.csv b/demo/scenarios/data/FlowerModelv1_TestSetResults_noB.csv
new file mode 100644
index 000000000..5d9905b2f
--- /dev/null
+++ b/demo/scenarios/data/FlowerModelv1_TestSetResults_noB.csv
@@ -0,0 +1,6150 @@
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diff --git a/demo/scenarios/data/FlowerModelv1_TestSetResults_noG.csv b/demo/scenarios/data/FlowerModelv1_TestSetResults_noG.csv
new file mode 100644
index 000000000..602553d78
--- /dev/null
+++ b/demo/scenarios/data/FlowerModelv1_TestSetResults_noG.csv
@@ -0,0 +1,6150 @@
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diff --git a/demo/scenarios/data/FlowerModelv1_TestSetResults_noR.csv b/demo/scenarios/data/FlowerModelv1_TestSetResults_noR.csv
new file mode 100644
index 000000000..0e27062bb
--- /dev/null
+++ b/demo/scenarios/data/FlowerModelv1_TestSetResults_noR.csv
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diff --git a/demo/scenarios/data/OxfordFlowerLabels.csv b/demo/scenarios/data/OxfordFlowerLabels.csv
new file mode 100644
index 000000000..3ef348ed2
--- /dev/null
+++ b/demo/scenarios/data/OxfordFlowerLabels.csv
@@ -0,0 +1,105 @@
+,,,,,Scientific Name,,,,,,,,,,
+Group,Count,Common Name,Other Name,Risk,Kingdom,Phylum,Class,Phylum,Clade1,Clade2,Clade 3,Order ,Family,Subfamily,Genus
+,,,,,,,,,,,,,,,
+1,40,primrose,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Ericales,Primulaceae,,Primula
+3,40,canterbury bells,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Campanulaceae,,Campanula
+7,40,moon orchild,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Asparagales,Orchidaceae,Epidendroideae,Phalaenopsis
+21,40,fire lily,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Liliales,Colchicaceae,,Gloriosa
+27,40,prince of whales feather,,G4,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Caryophyllales,Amaranthaceae,,Amaranthus
+34,40, mexican aster,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,,Cosmos
+45,40,bolero deep blue,texas bluebell,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Gentianales,Gentianaceae,,Eustoma
+16,41,globe flower,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Ranunculales,Ranunculaceae,Ranunculoideae,Trollus
+25,41,grape hyacinth,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Asparagales,Asparagaceae,Scilloideae,Muscari
+26,41,corn poppy,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Ranunculales,Papaveraceae,,Papaver
+39,41,siam tulip,,NT,Plantae,,,Tracheophytes,Angiosperms,Monocots,Commelinids,Zingiberales,Zingiberaceae,,Curcuma
+79,41,toad lily,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Liliales,Liliaceae,Calochortoideae,Tricyrtis
+24,42,red ginger,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,Commelinids,Zingiberales,Zingiberaceae,,Alpinia
+67,42,spring crocus,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Asparagales,Iridaceae,,Crocus
+35,43,alpine sea holly,,NT,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Apiales,Apiaceae,,Eryngium
+6,45,tiger lily,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Liliales,Liliaceae,Liliodeae,Lilium
+10,45,globe thistle,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,Carduoideae,Echinops
+32,45,garden phlox,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Ericales,Polemoniaceae,,Phlox
+9,46,monkshood,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Ranunculales,Ranunculaceae,,Aconitum
+33,46,love in the mist,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Ranunculales,Ranunculaceae,,Nigella
+93,46,ball moss,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,Commelinids,Poales,Bromeliaceae,,Tillandsia
+14,48,spear thistle,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,,Cirsium
+102,48,blackberry lily,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Asparagales,Iridaceae,,Iris
+13,49,king protea,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Proteales,Proteaceae,,Protea
+15,49,yellow iris,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Asparagales,Iridaceae,,Iris
+19,49,ballon flower,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Campanulaceae,Campanuloideae,Platycodon
+49,49,oxeye daisy,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,,Leucanthemum
+100,49,blanket flower,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,Asteroideae,Gaillardia
+50,50,common dandelion,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,Cichorioideae,Taraxacum
+61,50,cautleya spicata,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,Commelinids,Zingiberales,Zingiberaceae,,Cautleya
+31,52,carnation,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Caryophyllales,Caaryphyllaceae,,Dianthis
+64,52,silverbush,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Solanales,Convolvulaceae,,Convolvulus
+63,54,black-eyed susan,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,,Rudbeckia
+62,55,japanese anemone,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Ranunculales,Ranunculaceae,,Eriocapitella
+4,56,swear pea,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Fabales,Fabaceae,,Lathyrus
+20,56,arum lily,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Alismatales,Araceae,Aroideae,Zantedeschia
+38,56,great masterwort,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Apiales,Apiaceae,,Astrantia
+86,58,tree mallow,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Malvales,Malvaceae,Malvoideae,Malva
+101,58,trumpet creeper,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Lamiales,Bignoniaceae,,Capsis
+22,59,pincushion flower,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Dipsacales,Caprifolicaceae,,Scabiosa
+42,59,daffodil,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Asparagales,Amaryllidaceae,Amaryllidoideae,Narcissus
+2,60,hard-leaved pocket orchid,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Asparagales,Orchidaceae,Cypripedioideae,Paphiopedilum
+54,61,sunflower,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,,Helianthus
+66,61,osteospermum,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,Asteroideae,Osteospermum
+70,62,tree poppy,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Ranunculales,Papaveraceae,Papaveroideae,Dendromecon
+85,63,desert rose,,G4,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Rosales,Rosaceae,,Rosa
+87,63,magnolia,,,Plantae,,,Tracheophytes,Angiosperms,Magnoliids,,Magnoliales,Magnoliaceae,,Magnolia
+99,63,bromelia,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,Commelinids,Poales,Bromeliaceae,Bromelioideae,Bromelia
+5,65,english marigold,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,,Calendula
+28,66,stemless gentain,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Gentianales,Gentianaceae,,Gentiana
+92,66,bee balm,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Lamiales,Lamiaceae,Nepeoideae,Monarda
+97,66,mallow,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Malvales,Malvaceae,Malvoideae,Malva
+40,67,lenten rose,hellebore,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Ranunculales,Ranunculaceae,Ranunculoideae,Helleborus
+47,67,marigold,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,Asteroideae,Calendula
+57,67,gaura,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Myrtales,Onagraceae,Onagroideae,Gaura
+59,67,orange dahlia,mexican sunflower,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,,Tithonia
+68,68,bearded iris,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Asparagales,Iridaceae,Iridoideae,Iris
+69,68,windflower,anemone,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Ranunculales,Ranunculaceae,Ranunculoideae,Anemone
+48,71,buttercup,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Ranunculales,Ranunculaceae,Ranunculoideae,Ranunculus
+55,71,pelargonium,hibiscus,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Gentianales,Gentianaceae,,Pelargonium
+36,75,cattleya,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Asparagales,Orchidaceae,Epidendroideae,Cattleya
+91,76,hippesatrum,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Asparagales,Amaryllidaceae,Amaryllidoideae,Hippeastrum
+29,78,artichoke,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,,Cynara
+71,78,gazania,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,,Gazania
+18,82,peruvian lily,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Liliales,Alstromeriaceae,,Alstroemeria
+90,82,canna lily,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,Commelinids,Zingiberales,Cannaceae,,Canna
+98,82,mexican petunia,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Lamiales,Acanthaceae,,Ruellia
+8,85,bird of paradise,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,Commelinids,Zingiberales,Strelitziaceae,,Strelitzia
+17,85,purple coneflower,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,,Echinacea
+30,85,sweet william,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Caryophyllales,Caryophyllaceae,,Dianthis
+52,85,wild pansy,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Magnoliales,Violaceae,,Viola
+84,86,columbine,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Ranunculales,Ranunculaceae,Thailctroideae,Aquilegia
+11,87,snapdragon,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Lamiales,Plantaginaceae,,Antirrhium
+12,87,coltsfoot,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,Asteroideae,Tussilago
+23,91,fritillary,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Liliales,Liliaceae,Liliodeae,Fritillaria
+96,91,camellia,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Ericales,Theaceae,,Camellia
+44,93,poinsettia,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Malpighiales,Euphorbiaceae,,Euphorbia
+53,93,primula,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Ericales,Primulaceae,Primuloideae,Primula
+72,96,azalea,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Ericales,Ericaceae,,Rhododendron
+65,102,california poppy,,G4,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Ranunculales,Papaveraceae,,Eschscholzia
+80,105,anthurium,,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Alismatales,Araceae,Pothoideae,Anthurium
+76,107,morning glory,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Solanales,Convolvulaceae,,Ipomoea
+37,108,cape flower,guernesy lily,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Asparagales,Amaryllidaceae,Amaryllidoideae,Nerine
+56,109,bishop of llandaf dahlia,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,Asteroideae,Dahlia
+60,109,dahlia,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,Asteroideae,Dahlia
+82,112,clematis,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Ranunculales,Ranunculaceae,Ranunculoideae,Clematis
+58,114,geranium,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Geraniales,Geraniaceae,,Geranium
+75,120,thorn apple, jimsonweed,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Solanales,Solanaceae,,Datura
+41,127,barberton daisy,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Asterales,Asteraceae,,Gerbera
+95,128,bougainvillea,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Caryophyllales,Nyctaginaceae,,Bougainvillea
+43,130,sword lily,gladiolus,,Plantae,,,Tracheophytes,Angiosperms,Monocots,,Asparagales,Iridaceae,Crocideae,Gladiolus
+83,131,hibiscus,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Malvales,Malvaceae,Malvoideae,Hibiscus
+78,137,lotus,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,,Proteales,Nelumbonaceae,,Nelumbo
+88,154,cyclamen,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Ericales,Primulaceae,Myrisinoideae,Cyclamen
+94,162,foxglove,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Lamiales,Plantaginaceae,,Digitalis
+81,166,frangipani,plumeria,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Gentianales,Apocynaceae,,Plumeria
+74,171,rose,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Rosales,Rosaceae,Rosoideae,Rosa
+89,184,watercress,nasturtium,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Brassicales,Brassicaeae,,Nasturtium
+73,194,water lily,,,Plantae,,,Tracheophytes,Angiosperms,,,Nymphaeales,Nymphaeaceae,,
+46,196,wallflower,erysimum,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Brassicales,Brassicaeae,,Erysimum
+77,251,passion flower,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Rosids,Malpighiales,Passifloraceae,,Passiflora
+51,258,petunia,,,Plantae,,,Tracheophytes,Angiosperms,Eudicots,Asterids,Solanales,Solanaceae,Petuniodeae,Petunia
\ No newline at end of file
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diff --git a/demo/scenarios/evidence.ipynb b/demo/scenarios/evidence.ipynb
new file mode 100644
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+++ b/demo/scenarios/evidence.ipynb
@@ -0,0 +1,820 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Collect Evidence\n",
+    "\n",
+    "In the second phase of SDMT, we collect _evidence_ to attest to the fact that the model realized the properties specified in the previous phase.\n",
+    "\n",
+    "We define and instantiate `Measurement`s to generate this evidence. Each individual piece of evidence is a `Value`. Once `Value`s are produced, we can persist them to an _artifact store_ to maintain our evidence across sessions. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize MLTE Context\n",
+    "\n",
+    "MLTE contains a global context that manages the currently active _session_. Initializing the context tells MLTE how to store all of the artifacts that it produces."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from mlte.session import set_context, set_store\n",
+    "\n",
+    "store_path = os.path.join(os.getcwd(), \"store\")\n",
+    "os.makedirs(\n",
+    "    store_path, exist_ok=True\n",
+    ")  # Ensure we are creating the folder if it is not there.\n",
+    "\n",
+    "set_context(\"ns\", \"OxfordFlower\", \"0.0.1\")\n",
+    "set_store(f\"local://{store_path}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define different folders that will be used as input or output for the data gathering process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "# The path at which datasets are stored\n",
+    "DATASETS_DIR = Path.cwd() / \"data\"\n",
+    "\n",
+    "# Path where the model files are stored.\n",
+    "MODELS_DIR = Path.cwd() / \"model\"\n",
+    "\n",
+    "# The path at which media is stored\n",
+    "MEDIA_DIR = Path.cwd() / \"media\"\n",
+    "os.makedirs(MEDIA_DIR, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Download the model that will be used for some of these measurements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!sh get_model.sh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next sections, we will define additional functions and gather evidence for the different QA scenarios."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fairnesss QAS Measurements\n",
+    "\n",
+    "Evidence collected in this section checks for the Fairness scenario defined in the previous step. Note that some functions will be loaded from exernal Python files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# General functions.\n",
+    "\n",
+    "import garden\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def load_data(data_folder: str):\n",
+    "    \"\"\"Loads all garden data results and taxonomy categories.\"\"\"\n",
+    "    df_results = garden.load_base_results(data_folder)\n",
+    "    df_results.head()\n",
+    "\n",
+    "    # Load the taxonomic data and merge with results.\n",
+    "    df_info = garden.load_taxonomy(data_folder)\n",
+    "    df_results.rename(columns={\"label\": \"Label\"}, inplace=True)\n",
+    "    df_all = garden.merge_taxonomy_with_results(df_results, df_info)\n",
+    "\n",
+    "    return df_info, df_all\n",
+    "\n",
+    "\n",
+    "def split_data(df_info, df_all):\n",
+    "    \"\"\"Splits the data into 3 different populations to evaluate them.\"\"\"\n",
+    "    df_gardenpop = df_info.copy()\n",
+    "    df_gardenpop[\"Population1\"] = (\n",
+    "        np.around(\n",
+    "            np.random.dirichlet(np.ones(df_gardenpop.shape[0]), size=1)[0],\n",
+    "            decimals=3,\n",
+    "        )\n",
+    "        * 1000\n",
+    "    ).astype(int)\n",
+    "    df_gardenpop[\"Population2\"] = (\n",
+    "        np.around(\n",
+    "            np.random.dirichlet(np.ones(df_gardenpop.shape[0]), size=1)[0],\n",
+    "            decimals=3,\n",
+    "        )\n",
+    "        * 1000\n",
+    "    ).astype(int)\n",
+    "    df_gardenpop[\"Population3\"] = (\n",
+    "        np.around(\n",
+    "            np.random.dirichlet(np.ones(df_gardenpop.shape[0]), size=1)[0],\n",
+    "            decimals=3,\n",
+    "        )\n",
+    "        * 1000\n",
+    "    ).astype(int)\n",
+    "    df_gardenpop\n",
+    "\n",
+    "    # build populations from test data set that match the garden compositions\n",
+    "    from random import choices\n",
+    "\n",
+    "    # build 3 gardens with populations of 1000.\n",
+    "    pop_names = [\"Population1\", \"Population2\", \"Population3\"]\n",
+    "    gardenpops = np.zeros((3, 1000), int)\n",
+    "    gardenmems = np.zeros((3, 1000), int)\n",
+    "\n",
+    "    for j in range(1000):\n",
+    "        for i in range(len(df_gardenpop)):\n",
+    "            my_flower = df_gardenpop.iloc[i][\"Common Name\"]\n",
+    "\n",
+    "            for g in range(3):\n",
+    "                n_choices = df_gardenpop.iloc[i][pop_names[g]]\n",
+    "                my_choices = df_all[df_all[\"Common Name\"] == my_flower][\n",
+    "                    \"model correct\"\n",
+    "                ].to_list()\n",
+    "                my_selection = choices(my_choices, k=n_choices)\n",
+    "\n",
+    "                gardenpops[g][j] += sum(my_selection)\n",
+    "                gardenmems[g][j] += len(my_selection)\n",
+    "\n",
+    "    gardenpops\n",
+    "\n",
+    "    return gardenpops, gardenmems\n",
+    "\n",
+    "\n",
+    "def calculate_model_performance_acc(gardenpops, gardenmems):\n",
+    "    \"\"\"Get accucray of models across the garden populations\"\"\"\n",
+    "    gardenacc = np.zeros((3, 1000), float)\n",
+    "    for i in range(1000):\n",
+    "        for g in range(3):\n",
+    "            gardenacc[g][i] = gardenpops[g][i] / gardenmems[g][i]\n",
+    "    gardenacc\n",
+    "\n",
+    "    model_performance_acc = []\n",
+    "    for g in range(3):\n",
+    "        avg = round(np.average(gardenacc[g][:]), 3)\n",
+    "        std = round(np.std(gardenacc[g][:]), 3)\n",
+    "        min = round(np.amin(gardenacc[g][:]), 3)\n",
+    "        max = round(np.amax(gardenacc[g][:]), 3)\n",
+    "        model_performance_acc.append(round(avg, 3))\n",
+    "\n",
+    "        print(\"%1d %1.3f %1.3f %1.3f %1.3f\" % (g, avg, std, min, max))\n",
+    "\n",
+    "    return model_performance_acc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepare the data. For this section, instead of executing the model, we will use CSV files containing the results of an already executed run of the model.\n",
+    "data = load_data(DATASETS_DIR)\n",
+    "split_data = split_data(data[0], data[1])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this first example, we simply wrap the output from `accuracy_score` with a custom `Result` type to cope with the output of a third-party library that is not supported by a MLTE builtin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from values.multiple_accuracy import MultipleAccuracy\n",
+    "from mlte.measurement import ExternalMeasurement\n",
+    "\n",
+    "# Evaluate accuracy, identifier has to be the same one defined in the Spec.\n",
+    "accuracy_measurement = ExternalMeasurement(\n",
+    "    \"accuracy across gardens\", MultipleAccuracy, calculate_model_performance_acc\n",
+    ")\n",
+    "accuracy = accuracy_measurement.evaluate(split_data[0], split_data[1])\n",
+    "\n",
+    "# Inspect value\n",
+    "print(accuracy)\n",
+    "\n",
+    "# Save to artifact store\n",
+    "accuracy.save(force=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Robustness QAS Measurements\n",
+    "\n",
+    "Evidence collected in this section checks for the Robustness scenarios."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# General functions.\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "def calculate_base_accuracy(df_results: pd.DataFrame) -> pd.DataFrame:\n",
+    "    # Calculate the base model accuracy result per data label\n",
+    "    df_pos = (\n",
+    "        df_results[df_results[\"model correct\"] == True].groupby(\"label\").count()\n",
+    "    )\n",
+    "    df_pos.drop(columns=[\"prediced_label\"], inplace=True)\n",
+    "    df_neg = (\n",
+    "        df_results[df_results[\"model correct\"] == False]\n",
+    "        .groupby(\"label\")\n",
+    "        .count()\n",
+    "    )\n",
+    "    df_neg.drop(columns=[\"prediced_label\"], inplace=True)\n",
+    "    df_neg.rename(columns={\"model correct\": \"model incorrect\"}, inplace=True)\n",
+    "    df_res = df_pos.merge(\n",
+    "        df_neg, right_on=\"label\", left_on=\"label\", how=\"outer\"\n",
+    "    )\n",
+    "    df_res.fillna(0, inplace=True)\n",
+    "    df_res[\"model acc\"] = df_res[\"model correct\"] / (\n",
+    "        df_res[\"model correct\"] + df_res[\"model incorrect\"]\n",
+    "    )\n",
+    "    df_res[\"count\"] = df_res[\"model correct\"] + df_res[\"model incorrect\"]\n",
+    "    df_res.drop(columns=[\"model correct\", \"model incorrect\"], inplace=True)\n",
+    "    df_res.head()\n",
+    "\n",
+    "    return df_res\n",
+    "\n",
+    "\n",
+    "def calculate_accuracy_per_set(\n",
+    "    data_folder: str, df_results: pd.DataFrame, df_res: pd.DataFrame\n",
+    ") -> pd.DataFrame:\n",
+    "    # Calculate the model accuracy per data label for each blurred data set\n",
+    "    base_filename = \"FlowerModelv1_TestSetResults\"\n",
+    "    ext_filename = \".csv\"\n",
+    "    set_filename = [\"_blur2x8\", \"_blur5x8\", \"_blur0x8\", \"_noR\", \"_noG\", \"_noB\"]\n",
+    "\n",
+    "    col_root = \"model acc\"\n",
+    "\n",
+    "    for fs in set_filename:\n",
+    "        filename = os.path.join(data_folder, base_filename + fs + ext_filename)\n",
+    "        colname = col_root + fs\n",
+    "\n",
+    "        df_temp = pd.read_csv(filename)\n",
+    "        df_temp.drop(columns=[\"Unnamed: 0\"], inplace=True)\n",
+    "\n",
+    "        df_pos = (\n",
+    "            df_temp[df_temp[\"model correct\"] == True].groupby(\"label\").count()\n",
+    "        )\n",
+    "        df_pos.drop(columns=[\"prediced_label\"], inplace=True)\n",
+    "        df_neg = (\n",
+    "            df_results[df_results[\"model correct\"] == False]\n",
+    "            .groupby(\"label\")\n",
+    "            .count()\n",
+    "        )\n",
+    "        df_neg.drop(columns=[\"prediced_label\"], inplace=True)\n",
+    "        df_neg.rename(\n",
+    "            columns={\"model correct\": \"model incorrect\"}, inplace=True\n",
+    "        )\n",
+    "        df_res2 = df_pos.merge(\n",
+    "            df_neg, right_on=\"label\", left_on=\"label\", how=\"outer\"\n",
+    "        )\n",
+    "        df_res2.fillna(0, inplace=True)\n",
+    "\n",
+    "        df_res2[colname] = df_res2[\"model correct\"] / (\n",
+    "            df_res2[\"model correct\"] + df_res2[\"model incorrect\"]\n",
+    "        )\n",
+    "        df_res2.drop(columns=[\"model correct\", \"model incorrect\"], inplace=True)\n",
+    "\n",
+    "        df_res = df_res.merge(\n",
+    "            df_res2, right_on=\"label\", left_on=\"label\", how=\"outer\"\n",
+    "        )\n",
+    "\n",
+    "    df_res.head()\n",
+    "    return df_res\n",
+    "\n",
+    "\n",
+    "def print_model_accuracy(df_res: pd.DataFrame, key: str, name: str):\n",
+    "    model_acc = sum(df_res[key] * df_res[\"count\"]) / sum(df_res[\"count\"])\n",
+    "    print(name, model_acc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepare all data. Same as the case above, we will use CSV files that contain results of a previous execution of the model.\n",
+    "df_results = garden.load_base_results(DATASETS_DIR)\n",
+    "df_res = calculate_base_accuracy(df_results)\n",
+    "df_res = calculate_accuracy_per_set(DATASETS_DIR, df_results, df_res)\n",
+    "df_info = garden.load_taxonomy(DATASETS_DIR)\n",
+    "df_all = garden.merge_taxonomy_with_results(df_res, df_info, \"label\", \"Label\")\n",
+    "\n",
+    "# fill in missing model accuracy data\n",
+    "df_all[\"model acc_noR\"].fillna(0, inplace=True)\n",
+    "df_all[\"model acc_noG\"].fillna(0, inplace=True)\n",
+    "df_all[\"model acc_noB\"].fillna(0, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now do the actual measurements. First simply see the model accuracy across blurs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# view changes in model accuracy\n",
+    "print_model_accuracy(df_res, \"model acc\", \"base model accuracy\")\n",
+    "print_model_accuracy(\n",
+    "    df_res, \"model acc_blur2x8\", \"model accuracy with 2x8 blur\"\n",
+    ")\n",
+    "print_model_accuracy(\n",
+    "    df_res, \"model acc_blur5x8\", \"model accuracy with 5x8 blur\"\n",
+    ")\n",
+    "print_model_accuracy(\n",
+    "    df_res, \"model acc_blur0x8\", \"model accuracy with 0x8 blur\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Measure the ranksums (p-value) for all blur cases, using `scipy.stats.ranksums` and the `ExternalMeasurement` wrapper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "from values.ranksums import RankSums\n",
+    "from mlte.measurement import ExternalMeasurement\n",
+    "\n",
+    "my_blur = [\"2x8\", \"5x8\", \"0x8\"]\n",
+    "for i in range(len(my_blur)):\n",
+    "    # Define measurements.\n",
+    "    ranksum_measurement = ExternalMeasurement(\n",
+    "        f\"ranksums blur{my_blur[i]}\", RankSums, scipy.stats.ranksums\n",
+    "    )\n",
+    "\n",
+    "    # Evaluate.\n",
+    "    ranksum: RankSums = ranksum_measurement.evaluate(\n",
+    "        df_res[\"model acc\"], df_res[f\"model acc_blur{my_blur[i]}\"]\n",
+    "    )\n",
+    "\n",
+    "    # Inspect values\n",
+    "    print(ranksum)\n",
+    "\n",
+    "    # Save to artifact store\n",
+    "    ranksum.save(force=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now to next part of the question- is this equal across the phylogenic groups?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we will check the effect of blur for Clade 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from values.multiple_ranksums import MultipleRanksums\n",
+    "\n",
+    "# use the initial result, blur columns to anaylze effect of blur\n",
+    "df_all[\"delta_2x8\"] = df_all[\"model acc\"] - df_all[\"model acc_blur2x8\"]\n",
+    "df_all[\"delta_5x8\"] = df_all[\"model acc\"] - df_all[\"model acc_blur5x8\"]\n",
+    "df_all[\"delta_0x8\"] = df_all[\"model acc\"] - df_all[\"model acc_blur0x8\"]\n",
+    "\n",
+    "pops = df_all[\"Clade2\"].unique().tolist()\n",
+    "blurs = [\n",
+    "    \"delta_2x8\",\n",
+    "    \"delta_5x8\",\n",
+    "    \"delta_0x8\",\n",
+    "]\n",
+    "\n",
+    "ranksums: List = []\n",
+    "for i in range(len(blurs)):\n",
+    "    for pop1 in pops:\n",
+    "        for pop2 in pops:\n",
+    "            ranksum_measurement = ExternalMeasurement(\n",
+    "                f\"ranksums clade2 {pop1}-{pop2} blur{blurs[i]}\",\n",
+    "                RankSums,\n",
+    "                scipy.stats.ranksums,\n",
+    "            )\n",
+    "            ranksum: RankSums = ranksum_measurement.evaluate(\n",
+    "                df_all[df_all[\"Clade2\"] == pop1][blurs[i]],\n",
+    "                df_all[df_all[\"Clade2\"] == pop2][blurs[i]],\n",
+    "            )\n",
+    "            print(f\"blur {blurs[i]}: {ranksum}\")\n",
+    "            ranksums.append({ranksum.identifier: ranksum.array})\n",
+    "\n",
+    "multiple_ranksums_meas = ExternalMeasurement(\n",
+    "    f\"multiple ranksums for clade2\", MultipleRanksums, lambda x: x\n",
+    ")\n",
+    "multiple_ranksums: MultipleRanksums = multiple_ranksums_meas.evaluate(ranksums)\n",
+    "multiple_ranksums.num_pops = len(pops)\n",
+    "multiple_ranksums.save(force=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we check between clade 2 and clade 3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_now = (\n",
+    "    df_all[[\"Clade2\", \"Clade 3\"]]\n",
+    "    .copy()\n",
+    "    .groupby([\"Clade2\", \"Clade 3\"])\n",
+    "    .count()\n",
+    "    .reset_index()\n",
+    ")\n",
+    "ps1 = df_now[\"Clade2\"].to_list()\n",
+    "ps2 = df_now[\"Clade 3\"].to_list()\n",
+    "print(df_now)\n",
+    "\n",
+    "ranksums: List = []\n",
+    "for k in range(len(blurs)):\n",
+    "    print(\"\\n\", blurs[k])\n",
+    "    for i in range(len(ps1)):\n",
+    "        p1c1 = ps1[i]\n",
+    "        p1c2 = ps2[i]\n",
+    "        for j in range(len(ps1)):\n",
+    "            p2c1 = ps1[j]\n",
+    "            p2c2 = ps2[j]\n",
+    "            if (\n",
+    "                len(\n",
+    "                    df_all[\n",
+    "                        (df_all[\"Clade2\"] == p1c1) & (df_all[\"Clade 3\"] == p2c2)\n",
+    "                    ][blurs[k]]\n",
+    "                )\n",
+    "                > 0\n",
+    "                | len(\n",
+    "                    df_all[\n",
+    "                        (df_all[\"Clade2\"] == p2c1) & (df_all[\"Clade 3\"] == p2c2)\n",
+    "                    ][blurs[k]]\n",
+    "                )\n",
+    "                > 0\n",
+    "            ):\n",
+    "                ranksum_measurement = ExternalMeasurement(\n",
+    "                    f\"ranksums {p1c1}-{p2c2} - {p2c1}-{p2c2} blur{blurs[k]}\",\n",
+    "                    RankSums,\n",
+    "                    scipy.stats.ranksums,\n",
+    "                )\n",
+    "                ranksum: RankSums = ranksum_measurement.evaluate(\n",
+    "                    df_all[\n",
+    "                        (df_all[\"Clade2\"] == p1c1) & (df_all[\"Clade 3\"] == p2c2)\n",
+    "                    ][blurs[k]],\n",
+    "                    df_all[\n",
+    "                        (df_all[\"Clade2\"] == p2c1) & (df_all[\"Clade 3\"] == p2c2)\n",
+    "                    ][blurs[k]],\n",
+    "                )\n",
+    "                ranksums.append({ranksum.identifier: ranksum.array})\n",
+    "\n",
+    "multiple_ranksums_meas = ExternalMeasurement(\n",
+    "    f\"multiple ranksums between clade2 and 3\", MultipleRanksums, lambda x: x\n",
+    ")\n",
+    "multiple_ranksums: MultipleRanksums = multiple_ranksums_meas.evaluate(ranksums)\n",
+    "multiple_ranksums.num_pops = len(ps1)\n",
+    "multiple_ranksums.save(force=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Performance QAS Measurements\n",
+    "\n",
+    "Now we collect stored, CPU and memory usage data when predicting with the model, for the Performance scenario. NOTE: the version of tensorflow used in this demo requires running it under Python 3.9 or higher."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the external script that will load and run the model for inference/prediction.\n",
+    "script = Path.cwd() / \"model_predict.py\"\n",
+    "args = [\n",
+    "    \"--images\",\n",
+    "    DATASETS_DIR,\n",
+    "    \"--model\",\n",
+    "    MODELS_DIR / \"model_f3_a.json\",\n",
+    "    \"--weights\",\n",
+    "    MODELS_DIR / \"model_f_a.h5\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlte.measurement.storage import LocalObjectSize\n",
+    "from mlte.value.types.integer import Integer\n",
+    "\n",
+    "store_measurement = LocalObjectSize(\"model size\")\n",
+    "size: Integer = store_measurement.evaluate(MODELS_DIR)\n",
+    "print(size)\n",
+    "size.save(force=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlte.measurement import ProcessMeasurement\n",
+    "from mlte.measurement.cpu import LocalProcessCPUUtilization, CPUStatistics\n",
+    "\n",
+    "cpu_measurement = LocalProcessCPUUtilization(\"predicting cpu\")\n",
+    "cpu_stats: CPUStatistics = cpu_measurement.evaluate(\n",
+    "    ProcessMeasurement.start_script(script, args)\n",
+    ")\n",
+    "print(cpu_stats)\n",
+    "cpu_stats.save(force=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlte.measurement.memory import (\n",
+    "    LocalProcessMemoryConsumption,\n",
+    "    MemoryStatistics,\n",
+    ")\n",
+    "\n",
+    "mem_measurement = LocalProcessMemoryConsumption(\"predicting memory\")\n",
+    "mem_stats: MemoryStatistics = mem_measurement.evaluate(\n",
+    "    ProcessMeasurement.start_script(script, args)\n",
+    ")\n",
+    "print(mem_stats)\n",
+    "mem_stats.save(force=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretability QAS Measurements.\n",
+    "\n",
+    "Now we proceed to gather data about the Interpretability of the model, for the corresponding scenario. NOTE: the version of tensorflow used in this demo requires running it under Python 3.9 or higher."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_filename = (\n",
+    "    MODELS_DIR / \"model_f3_a.json\"\n",
+    ")  # The json file of the model to load\n",
+    "weights_filename = MODELS_DIR / \"model_f_a.h5\"  # The weights file for the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from model_analysis import *\n",
+    "\n",
+    "# Load the model/\n",
+    "loaded_model = load_model(model_filename, weights_filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load and show the image.\n",
+    "\n",
+    "flower_img = \"flower3.jpg\"  # Filename of flower image to use, public domain image adapted from: https://commons.wikimedia.org/wiki/File:Beautiful_white_flower_in_garden.jpg\n",
+    "flower_idx = (\n",
+    "    42  # Classifier index of associated flower (see OxfordFlower102Labels.csv)\n",
+    ")\n",
+    "\n",
+    "im = read_image(os.path.join(DATASETS_DIR, flower_img))\n",
+    "\n",
+    "plt.imshow(im)\n",
+    "plt.axis(\"off\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = run_model(im, loaded_model)\n",
+    "\n",
+    "baseline, alphas = generate_baseline_and_alphas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interpolated_images = interpolate_images(\n",
+    "    baseline=baseline, image=im, alphas=alphas\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize=(20, 20))\n",
+    "\n",
+    "i = 0\n",
+    "for alpha, image in zip(alphas[0::10], interpolated_images[0::10]):\n",
+    "    i += 1\n",
+    "    plt.subplot(1, len(alphas[0::10]), i)\n",
+    "    plt.title(f\"alpha: {alpha:.1f}\")\n",
+    "    plt.imshow(image)\n",
+    "    plt.axis(\"off\")\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_gradients = compute_gradients(\n",
+    "    loaded_model=loaded_model,\n",
+    "    images=interpolated_images,\n",
+    "    target_class_idx=flower_idx,\n",
+    ")\n",
+    "print(path_gradients.shape)\n",
+    "\n",
+    "ig = integral_approximation(gradients=path_gradients)\n",
+    "print(ig.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ig_attributions = integrated_gradients(\n",
+    "    baseline=baseline,\n",
+    "    image=im,\n",
+    "    target_class_idx=flower_idx,\n",
+    "    loaded_model=loaded_model,\n",
+    "    m_steps=240,\n",
+    ")\n",
+    "print(ig_attributions.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plot_img_attributions(\n",
+    "    image=im,\n",
+    "    baseline=baseline,\n",
+    "    target_class_idx=flower_idx,\n",
+    "    loaded_model=loaded_model,\n",
+    "    m_steps=240,\n",
+    "    cmap=plt.cm.inferno,\n",
+    "    overlay_alpha=0.4,\n",
+    ")\n",
+    "\n",
+    "plt.savefig(MEDIA_DIR / \"attributions.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlte.measurement import ExternalMeasurement\n",
+    "from mlte.value.types.image import Image\n",
+    "\n",
+    "# Save to MLTE store.\n",
+    "img_collector = ExternalMeasurement(\"image attributions\", Image)\n",
+    "img = img_collector.ingest(MEDIA_DIR / \"attributions.png\")\n",
+    "img.save(force=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "82adda432962015d5f71beb9387a99f24d390514e497c776c87ff3434daf7312"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/demo/scenarios/garden.py b/demo/scenarios/garden.py
new file mode 100644
index 000000000..c17fd0d2d
--- /dev/null
+++ b/demo/scenarios/garden.py
@@ -0,0 +1,164 @@
+from os import path
+
+import pandas as pd
+
+
+def load_base_results(data_folder: str) -> pd.DataFrame:
+    df_results = pd.read_csv(
+        path.join(data_folder, "FlowerModelv1_TestSetResults.csv")
+    )
+    df_results.drop(columns=["Unnamed: 0"], inplace=True)
+    return df_results
+
+
+def load_taxonomy(data_folder: str) -> pd.DataFrame:
+    """Loads taxonomy info about the flowers and returns a dict with it."""
+    df_labels = pd.read_csv(
+        path.join(data_folder, "OxfordFlowerLabels.csv"), header=1
+    )
+    df_labels.drop(columns=["Phylum", "Class"], inplace=True)
+    df_labels.rename(columns={"Phylum.1": "Phylum"}, inplace=True)
+    df_labels.drop([0], inplace=True)
+    # df_labels['Common Name'] = df_labels['Common Name'].str.strip()
+    df_labels["Label Name"] = df_labels["Common Name"].replace(
+        " ", "_", regex=True
+    )
+    df_labels.head()
+
+    # set up a data lavel dictionary to switch between data label number and english name
+    label_dict = {
+        0: "_mexican_aster",
+        1: "alpine_sea_holly",
+        2: "anthurium",
+        3: "artichoke",
+        4: "arum_lily",
+        5: "azalea",
+        6: "ball_moss",
+        7: "ballon_flower",
+        8: "barberton_daisy",
+        9: "bearded_iris",
+        10: "bee_balm",
+        11: "bird_of_paradise",
+        12: "bishop_of_llandaf_dahlia",
+        13: "black-eyed_susan",
+        14: "blackberry_lily",
+        15: "blanket_flower",
+        16: "bolero_deep_blue",
+        17: "bougainvillea",
+        18: "bromelia",
+        19: "buttercup",
+        20: "california_poppy",
+        21: "camellia",
+        22: "canna_lily",
+        23: "canterbury_bells",
+        24: "cape_flower",
+        25: "carnation",
+        26: "cattleya",
+        27: "cautleya_spicata",
+        28: "clematis",
+        29: "coltsfoot",
+        30: "columbine",
+        31: "common_dandelion",
+        32: "corn_poppy",
+        33: "cyclamen",
+        34: "daffodil",
+        35: "dahlia",
+        36: "desert_rose",
+        37: "english_marigold",
+        38: "fire_lily",
+        39: "foxglove",
+        40: "frangipani",
+        41: "fritillary",
+        42: "garden_phlox",
+        43: "gaura",
+        44: "gazania",
+        45: "geranium",
+        46: "globe_flower",
+        47: "globe_thistle",
+        48: "grape_hyacinth",
+        49: "great_masterwort",
+        50: "hard-leaved_pocket_orchid",
+        51: "hibiscus",
+        52: "hippesatrum",
+        53: "japanese_anemone",
+        54: "king_protea",
+        55: "lenten_rose",
+        56: "lotus",
+        57: "love_in_the_mist",
+        58: "magnolia",
+        59: "mallow",
+        60: "marigold",
+        61: "mexican_petunia",
+        62: "monkshood",
+        63: "moon_orchild",
+        64: "morning_glory",
+        65: "osteospermum",
+        66: "oxeye_daisy",
+        67: "passion_flower",
+        68: "pelargonium",
+        69: "peruvian_lily",
+        70: "petunia",
+        71: "pincushion_flower",
+        72: "poinsettia",
+        73: "primrose",
+        74: "primula",
+        75: "prince_of_whales_feather",
+        76: "purple_coneflower",
+        77: "red_ginger",
+        78: "rose",
+        79: "siam_tulip",
+        80: "silverbush",
+        81: "snapdragon",
+        82: "spear_thistle",
+        83: "spring_crocus",
+        84: "stemless_gentain",
+        85: "sunflower",
+        86: "swear_pea",
+        87: "sweet_william",
+        88: "sword_lily",
+        89: "thorn_apple",
+        90: "tiger_lily",
+        91: "orange_dahlia",
+        92: "toad_lily",
+        93: "tree_mallow",
+        94: "tree_poppy",
+        95: "trumpet_creeper",
+        96: "wallflower",
+        97: "water_lily",
+        98: "watercress",
+        99: "wild_pansy",
+        100: "windflower",
+        101: "yellow_iris",
+    }
+    df_dict = pd.DataFrame.from_dict(
+        {"Label": list(label_dict), "Label Name": label_dict.values()}
+    )
+    df_dict.head()
+
+    # fill in missing data metadata
+    df_info = df_labels.merge(
+        df_dict, how="outer", left_on="Label Name", right_on="Label Name"
+    )
+    df_info["Clade1"].fillna("None", inplace=True)
+    df_info["Clade2"].fillna("None", inplace=True)
+    df_info["Clade 3"].fillna("None", inplace=True)
+    df_info["Subfamily"].fillna("None", inplace=True)
+    df_info["Genus"].fillna("None", inplace=True)
+    df_info["Risk"].fillna("None", inplace=True)
+
+    df_info
+    print(len(df_info), len(df_labels), len(df_dict))
+
+    return df_info
+
+
+def merge_taxonomy_with_results(
+    df_results: pd.DataFrame,
+    df_info: pd.DataFrame,
+    left_on: str = "Label",
+    right_on: str = "Label",
+) -> pd.DataFrame:
+    """Merge results with taxonomy."""
+    df_all = df_results.merge(df_info, left_on=left_on, right_on=right_on)
+    df_all
+    return df_all
diff --git a/demo/scenarios/get_model.sh b/demo/scenarios/get_model.sh
new file mode 100644
index 000000000..91690be6e
--- /dev/null
+++ b/demo/scenarios/get_model.sh
@@ -0,0 +1,2 @@
+#!/usr/bin/env bash
+wget 'https://docs.google.com/uc?export=download&id=15kAII1kOPGIAI46OP01ecNkq4tdf5yXw' -O ./model/model_f_a.h5
diff --git a/demo/scenarios/model/.gitignore b/demo/scenarios/model/.gitignore
new file mode 100644
index 000000000..dfc8249f9
--- /dev/null
+++ b/demo/scenarios/model/.gitignore
@@ -0,0 +1 @@
+*.h5
diff --git a/demo/scenarios/model/model_f3_a.json b/demo/scenarios/model/model_f3_a.json
new file mode 100644
index 000000000..a4a2c5590
--- /dev/null
+++ b/demo/scenarios/model/model_f3_a.json
@@ -0,0 +1 @@
+{"class_name": "Functional", "config": {"name": "model", "trainable": true, "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 224, 224, 3], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_2"}, "name": "input_2", "inbound_nodes": []}, {"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 224, 224, 3], "dtype": "float32", "sparse": false, "ragged": false, "name": "resizing_input"}}, {"class_name": "Resizing", "config": {"name": "resizing", "trainable": true, "dtype": "float32", "height": 230, "width": 230, "interpolation": "bilinear", "crop_to_aspect_ratio": false}}, {"class_name": "RandomFlip", "config": {"name": "random_flip", "trainable": true, "dtype": "float32", "mode": "horizontal_and_vertical", "seed": null}}, {"class_name": "RandomRotation", "config": {"name": "random_rotation", "trainable": true, "dtype": "float32", "factor": 0.2, "fill_mode": "reflect", "fill_value": 0.0, "interpolation": "bilinear", "seed": null}}, {"class_name": "RandomZoom", "config": {"name": "random_zoom", "trainable": true, "dtype": "float32", "height_factor": 0.3, "width_factor": null, "fill_mode": "reflect", "fill_value": 0.0, "interpolation": "bilinear", "seed": null}}, {"class_name": "RandomCrop", "config": {"name": "random_crop", "trainable": true, "dtype": "float32", "height": 224, "width": 224, "seed": null}}]}, "name": "sequential", "inbound_nodes": [[["input_2", 0, 0, {}]]]}, {"class_name": "SlicingOpLambda", "config": {"name": "tf.__operators__.getitem", "trainable": true, "dtype": "float32", "function": "__operators__.getitem"}, "name": "tf.__operators__.getitem", "inbound_nodes": [["sequential", 1, 0, {"slice_spec": [{"class_name": "__ellipsis__"}, {"start": null, "stop": null, "step": -1}]}]]}, {"class_name": "TFOpLambda", "config": {"name": "tf.nn.bias_add", "trainable": true, "dtype": "float32", "function": "nn.bias_add"}, "name": "tf.nn.bias_add", "inbound_nodes": [["tf.__operators__.getitem", 0, 0, {"bias": [-103.93900299072266, -116.77899932861328, -123.68000030517578], "data_format": "NHWC"}]]}, {"class_name": "Functional", "config": {"name": "resnet50", "trainable": true, "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 224, 224, 3], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "name": "input_1", "inbound_nodes": []}, {"class_name": "ZeroPadding2D", "config": {"name": "conv1_pad", "trainable": true, "dtype": "float32", "padding": [[3, 3], [3, 3]], "data_format": "channels_last"}, "name": "conv1_pad", "inbound_nodes": [[["input_1", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv1_conv", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [7, 7], "strides": [2, 2], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv1_conv", "inbound_nodes": [[["conv1_pad", 0, 0, {}]]]}, {"class_name": "BatchNormalization", "config": {"name": "conv1_bn", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 1.001e-05, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "conv1_bn", "inbound_nodes": [[["conv1_conv", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": 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"config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "conv5_block3_3_bn", "inbound_nodes": [[["conv5_block3_3_conv", 0, 0, {}]]]}, {"class_name": "Add", "config": {"name": "conv5_block3_add", "trainable": true, "dtype": "float32"}, "name": "conv5_block3_add", "inbound_nodes": [[["conv5_block2_out", 0, 0, {}], ["conv5_block3_3_bn", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "conv5_block3_out", "trainable": true, "dtype": "float32", "activation": "relu"}, "name": "conv5_block3_out", "inbound_nodes": [[["conv5_block3_add", 0, 0, {}]]]}], "input_layers": [["input_1", 0, 0]], "output_layers": [["conv5_block3_out", 0, 0]]}, "name": "resnet50", "inbound_nodes": [[["tf.nn.bias_add", 0, 0, {"training": false}]]]}, {"class_name": "GlobalAveragePooling2D", "config": {"name": "global_average_pooling2d", "trainable": true, "dtype": "float32", "data_format": "channels_last", "keepdims": false}, "name": "global_average_pooling2d", "inbound_nodes": [[["resnet50", 1, 0, {}]]]}, {"class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": "float32", "rate": 0.6, "noise_shape": null, "seed": null}, "name": "dropout", "inbound_nodes": [[["global_average_pooling2d", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1024, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": {"class_name": "L1L2", "config": {"l1": 9.999999747378752e-06, "l2": 9.999999747378752e-05}}, "bias_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-05}}, "activity_regularizer": {"class_name": "L2", "config": {"l2": 9.999999747378752e-06}}, "kernel_constraint": null, "bias_constraint": null}, "name": "dense", "inbound_nodes": [[["dropout", 0, 0, {}]]]}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.2, "noise_shape": null, "seed": null}, "name": "dropout_1", "inbound_nodes": [[["dense", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 102, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_1", "inbound_nodes": [[["dropout_1", 0, 0, {}]]]}], "input_layers": [["input_2", 0, 0]], "output_layers": [["dense_1", 0, 0]]}, "keras_version": "2.12.0", "backend": "tensorflow"}
\ No newline at end of file
diff --git a/demo/scenarios/model_analysis.py b/demo/scenarios/model_analysis.py
new file mode 100644
index 000000000..e406f9d6b
--- /dev/null
+++ b/demo/scenarios/model_analysis.py
@@ -0,0 +1,161 @@
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from tensorflow.keras.models import model_from_json
+
+
+def load_model(model_filename: str, weights_filename: str):
+    # Load model
+    json_file = open(model_filename, "r")
+    loaded_model_json = json_file.read()
+    loaded_model = model_from_json(loaded_model_json)
+    json_file.close()
+
+    # Load weights into new model
+    loaded_model.load_weights(weights_filename)
+
+    return loaded_model
+
+
+def run_model(im, loaded_model):
+    im_batch = tf.expand_dims(im, 0)
+    predictions = loaded_model(im_batch)
+    return predictions
+
+
+def read_image(filename):
+    image = tf.io.read_file(filename)
+    image = tf.io.decode_jpeg(image, channels=3)
+    image = tf.image.convert_image_dtype(image, tf.float32)
+    image = tf.image.resize_with_pad(image, target_height=224, target_width=224)
+    return image
+
+
+def generate_baseline_and_alphas():
+    baseline = tf.zeros(shape=(224, 224, 3))
+    m_steps = 50
+    alphas = tf.linspace(
+        start=0.0, stop=1.0, num=m_steps + 1
+    )  # Generate m_steps intervals for integral_approximation() below.
+    return baseline, alphas
+
+
+def interpolate_images(baseline, image, alphas):
+    alphas_x = alphas[:, tf.newaxis, tf.newaxis, tf.newaxis]
+    baseline_x = tf.expand_dims(baseline, axis=0)
+    input_x = tf.expand_dims(image, axis=0)
+    delta = input_x - baseline_x
+    images = baseline_x + alphas_x * delta
+    return images
+
+
+def integral_approximation(gradients):
+    # riemann_trapezoidal
+    grads = (gradients[:-1] + gradients[1:]) / tf.constant(2.0)
+    int_gradients = tf.math.reduce_mean(grads, axis=0)
+    return int_gradients
+
+
+def compute_gradients(loaded_model, images, target_class_idx):
+    with tf.GradientTape() as tape:
+        tape.watch(images)
+        probs = loaded_model(images)[:, target_class_idx]
+        """
+    If your model does not have a softmax output layer,
+    comment out the above line and un-comment the following 2 lines
+    """
+        # logits = loaded_model(images)
+        # probs = tf.nn.softmax(logits, axis=-1)[:, target_class_idx]
+
+    return tape.gradient(probs, images)
+
+
+@tf.function
+def one_batch(baseline, image, alpha_batch, target_class_idx, loaded_model):
+    # Generate interpolated inputs between baseline and input.
+    interpolated_path_input_batch = interpolate_images(
+        baseline=baseline, image=image, alphas=alpha_batch
+    )
+
+    # Compute gradients between model outputs and interpolated inputs.
+    gradient_batch = compute_gradients(
+        loaded_model=loaded_model,
+        images=interpolated_path_input_batch,
+        target_class_idx=target_class_idx,
+    )
+    return gradient_batch
+
+
+def integrated_gradients(
+    baseline, image, target_class_idx, loaded_model, m_steps=50, batch_size=32
+):
+    # Generate alphas.
+    alphas = tf.linspace(start=0.0, stop=1.0, num=m_steps + 1)
+
+    # Collect gradients.
+    gradient_batches = []
+
+    # Iterate alphas range and batch computation for speed, memory efficiency, and scaling to larger m_steps.
+    for alpha in tf.range(0, len(alphas), batch_size):
+        from_ = alpha
+        to = tf.minimum(from_ + batch_size, len(alphas))
+        alpha_batch = alphas[from_:to]
+
+        gradient_batch = one_batch(
+            baseline, image, alpha_batch, target_class_idx, loaded_model
+        )
+        gradient_batches.append(gradient_batch)
+
+    # Concatenate path gradients together row-wise into single tensor.
+    total_gradients = tf.concat(gradient_batches, axis=0)
+
+    # Integral approximation through averaging gradients.
+    avg_gradients = integral_approximation(gradients=total_gradients)
+
+    # Scale integrated gradients with respect to input.
+    gradients = (image - baseline) * avg_gradients
+    return gradients
+
+
+def plot_img_attributions(
+    baseline,
+    image,
+    target_class_idx,
+    loaded_model,
+    m_steps=50,
+    cmap=None,
+    overlay_alpha=0.4,
+):
+    attributions = integrated_gradients(
+        baseline=baseline,
+        image=image,
+        target_class_idx=target_class_idx,
+        loaded_model=loaded_model,
+        m_steps=m_steps,
+    )
+
+    # Sum of the attributions across color channels for visualization.
+    # The attribution mask shape is a grayscale image with height and width
+    # equal to the original image.
+    attribution_mask = tf.reduce_sum(tf.math.abs(attributions), axis=-1)
+
+    fig, axs = plt.subplots(nrows=2, ncols=2, squeeze=False, figsize=(8, 8))
+
+    axs[0, 0].set_title("Baseline image")
+    axs[0, 0].imshow(baseline)
+    axs[0, 0].axis("off")
+
+    axs[0, 1].set_title("Original image")
+    axs[0, 1].imshow(image)
+    axs[0, 1].axis("off")
+
+    axs[1, 0].set_title("Attribution mask")
+    axs[1, 0].imshow(attribution_mask, cmap=cmap)
+    axs[1, 0].axis("off")
+
+    axs[1, 1].set_title("Overlay")
+    axs[1, 1].imshow(attribution_mask, cmap=cmap)
+    axs[1, 1].imshow(image, alpha=overlay_alpha)
+    axs[1, 1].axis("off")
+
+    plt.tight_layout()
+    return fig
diff --git a/demo/scenarios/model_predict.py b/demo/scenarios/model_predict.py
new file mode 100644
index 000000000..acf3ffbb6
--- /dev/null
+++ b/demo/scenarios/model_predict.py
@@ -0,0 +1,105 @@
+import argparse
+import os
+import sys
+import time
+from resource import *
+
+"""
+This script gets performance metrics (elapsed time and memory consumption) for running inference on a model.
+It does NOT check accurate predictions - it simply runs the inference on the provided dataset and outputs statics
+on elapsed time and memory consumption.
+"""
+
+# Command line args
+parser = argparse.ArgumentParser(
+    description="Command line arguments for the model performance tester."
+)
+parser.add_argument(
+    "--images",
+    help="The directory that contains the images to use for testing model performance.",
+    required=True,
+)
+# --model file example: model_f3_a.json
+parser.add_argument(
+    "--model", help="The json formatted model file.", required=True
+)
+# --weights file example: model_f_a.h5
+parser.add_argument(
+    "--weights", help="The file that contains the model weights.", required=True
+)
+args = parser.parse_args()
+
+# getrusage returns Kibibytes on linux and bytes on MacOS
+r_mem_units_str = "KiB" if sys.platform.startswith("linux") else "bytes"
+
+import tensorflow as tf
+
+print("TensorFlow version:", tf.__version__)
+
+from tensorflow.keras.models import model_from_json
+
+# Load dataset
+dataset = tf.keras.utils.image_dataset_from_directory(
+    args.images,
+    image_size=(224, 224),
+    labels=None,
+    label_mode="categorical",
+    batch_size=1,
+    shuffle=True,
+)
+
+ru1 = getrusage(RUSAGE_SELF).ru_maxrss
+
+# Load model
+json_file = open(args.model, "r")
+loaded_model_json = json_file.read()
+loaded_model = model_from_json(loaded_model_json)
+json_file.close()
+
+# Load weights into new model
+loaded_model.load_weights(args.weights)
+print("Loaded model from disk!")
+
+ru2 = getrusage(RUSAGE_SELF).ru_maxrss
+
+print(loaded_model.summary())
+
+mfile_size = os.path.getsize(args.model)
+wfile_size = os.path.getsize(args.weights)
+
+print(f"Size of model json file ({args.model}): {mfile_size} bytes")
+print(f"Size of weights file ({args.weights}): {wfile_size} bytes")
+print(
+    f"Memory used for the entire model loading process: {ru2 - ru1} {r_mem_units_str}."
+)
+
+total_elapsed_time = 0.0
+total_inference_memory = 0.0
+num_samples = len(dataset)
+print(f"Running inference on {num_samples} samples...")
+
+for image in dataset:
+    start = time.time()
+    ru3 = getrusage(RUSAGE_SELF).ru_maxrss
+    _ = loaded_model.predict(image)
+    ru4 = getrusage(RUSAGE_SELF).ru_maxrss
+    end = time.time()
+
+    elapsed_time = end - start
+    inference_memory = ru4 - ru3
+    total_elapsed_time += elapsed_time
+    total_inference_memory += inference_memory
+
+avg_elapsed_time = total_elapsed_time / num_samples
+avg_inference_memory = total_inference_memory / num_samples
+print("\n--- STATISTICS ---")
+print(
+    "Average elapsed time per inference: {0:.5f} seconds".format(
+        avg_elapsed_time
+    )
+)
+print(
+    "Average memory used per inference: {0:.5f} {1}.".format(
+        avg_inference_memory, r_mem_units_str
+    )
+)
diff --git a/demo/scenarios/properties/fairness.py b/demo/scenarios/properties/fairness.py
new file mode 100644
index 000000000..a56c2b1ae
--- /dev/null
+++ b/demo/scenarios/properties/fairness.py
@@ -0,0 +1,24 @@
+"""
+Fairness property definition.
+"""
+
+from mlte.property.base import Property
+
+
+class Fairness(Property):
+    """
+    Defintion of the Fairness property.
+    """
+
+    def __init__(self, rationale: str):
+        """Initialize a Fairness instance."""
+        super().__init__(
+            instance=self,
+            description="""
+                Fairness refers to the absence of biases in data and model inaccuracies that lead to models that treat individuals
+                or groups unfavorably on the basis of inherent or acquired characteristics (such as race, gender, disabilities,
+                or others). For ML models, this means ensuring similar model performance across specified subpopulations, groups,
+                or data.
+                """,
+            rationale=rationale,
+        )
diff --git a/demo/scenarios/properties/interpretability.py b/demo/scenarios/properties/interpretability.py
new file mode 100644
index 000000000..d11ba7ad5
--- /dev/null
+++ b/demo/scenarios/properties/interpretability.py
@@ -0,0 +1,24 @@
+"""
+Interpretability property definition.
+"""
+
+from mlte.property.base import Property
+
+
+class Interpretability(Property):
+    """
+    The Interpretability property
+    """
+
+    def __init__(self, rationale: str):
+        """Initialize a Interpretability instance."""
+        super().__init__(
+            instance=self,
+            description="""
+                Interpretability is the degree to which a human can understand the cause of a decision.
+                For ML models, it is about providing information for a human user to interpret and understand
+                what data was influential in the ML model's result. A model developer should return interpretability
+                evidence along with the model inference result.
+                """,
+            rationale=rationale,
+        )
diff --git a/demo/scenarios/properties/predicting_compute_cost.py b/demo/scenarios/properties/predicting_compute_cost.py
new file mode 100644
index 000000000..7c322f86c
--- /dev/null
+++ b/demo/scenarios/properties/predicting_compute_cost.py
@@ -0,0 +1,29 @@
+"""
+PredictingComputeCost property definition.
+"""
+
+from mlte.property.base import Property
+
+
+class PredictingComputeCost(Property):
+    """
+    The PredictingComputeCost property reflects the costs
+    of model predicting associated with compute resources.
+    """
+
+    def __init__(self, rationale: str):
+        """
+        Initialize a PredictingComputeCost instance.
+        """
+        super().__init__(
+            instance=self,
+            description="""
+                The PredictingComputeCost property assesses the
+                computational requirements of model predicting.
+                This might be measured in terms of CPU utilization
+                for property processes that run locally, or the cost
+                of compute resources required for property processes
+                that run on on-demand cloud infrastructure.
+                """,
+            rationale=rationale,
+        )
diff --git a/demo/scenarios/properties/predicting_memory_cost.py b/demo/scenarios/properties/predicting_memory_cost.py
new file mode 100644
index 000000000..f8d1a215a
--- /dev/null
+++ b/demo/scenarios/properties/predicting_memory_cost.py
@@ -0,0 +1,29 @@
+"""
+PredictingMemoryCost property definition.
+"""
+
+from mlte.property.base import Property
+
+
+class PredictingMemoryCost(Property):
+    """
+    The PredictingMemoryCost property reflects the cost
+    of model predicting associated with memory resources.
+    """
+
+    def __init__(self, rationale: str):
+        """
+        Initialize a PredictingMemoryCost instance.
+        """
+        super().__init__(
+            instance=self,
+            description="""
+                The PredictingMemoryCost property assesses the
+                memory requirements of model predicting. This might
+                be measured by the memory requirements of property
+                processes that run locally, or the cost of memory
+                resources required for property processes that run
+                on on-demand cloud infrastructure.
+                """,
+            rationale=rationale,
+        )
diff --git a/demo/scenarios/properties/robustness.py b/demo/scenarios/properties/robustness.py
new file mode 100644
index 000000000..4b21e162d
--- /dev/null
+++ b/demo/scenarios/properties/robustness.py
@@ -0,0 +1,23 @@
+"""
+Robustness property definition.
+"""
+
+from mlte.property.base import Property
+
+
+class Robustness(Property):
+    """
+    Definition of the Robustness property.
+    """
+
+    def __init__(self, rationale: str):
+        """Initialize a Robustness instance."""
+        super().__init__(
+            instance=self,
+            description="""
+                Robustness in general is the degree to which a system continues to function in the presence of
+                invalid inputs or stressful environmental conditions. For ML models, this means checking that
+                model performance does not deteriorate significantly in the presence of noise.
+                """,
+            rationale=rationale,
+        )
diff --git a/demo/scenarios/report.ipynb b/demo/scenarios/report.ipynb
new file mode 100644
index 000000000..8c1f421a9
--- /dev/null
+++ b/demo/scenarios/report.ipynb
@@ -0,0 +1,275 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Validation and Report Generation\n",
+    "\n",
+    "The final phase of SDMT involves aggregating evidence, validating the metrics reflected by the evidence we collected, and displaying this information in a report."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize MLTE Context\n",
+    "\n",
+    "MLTE contains a global context that manages the currently active _session_. Initializing the context tells MLTE how to store all of the artifacts that it produces."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from mlte.session import set_context, set_store\n",
+    "\n",
+    "store_path = os.path.join(os.getcwd(), \"store\")\n",
+    "os.makedirs(\n",
+    "    store_path, exist_ok=True\n",
+    ")  # Ensure we are creating the folder if it is not there.\n",
+    "\n",
+    "set_context(\"ns\", \"OxfordFlower\", \"0.0.1\")\n",
+    "set_store(f\"local://{store_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# The path at which reports are stored\n",
+    "REPORTS_DIR = Path(os.getcwd()) / \"reports\"\n",
+    "os.makedirs(REPORTS_DIR, exist_ok=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Validate Values and get an updated `ValidatedSpec` with `Result`s"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have our `Spec` ready and we have enough evidence, we create a `SpecValidator` with our spec, and add all the `Value`s we have. With that we can validate our spec and generate an output `ValidatedSpec`, with the validation results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlte.spec.spec import Spec\n",
+    "from mlte.validation.spec_validator import SpecValidator\n",
+    "\n",
+    "from mlte.measurement.cpu import CPUStatistics\n",
+    "from mlte.measurement.memory import MemoryStatistics\n",
+    "from mlte.value.types.image import Image\n",
+    "from mlte.value.types.integer import Integer\n",
+    "\n",
+    "from values.multiple_accuracy import MultipleAccuracy\n",
+    "from values.multiple_ranksums import MultipleRanksums\n",
+    "from values.ranksums import RankSums\n",
+    "\n",
+    "# Load the specification\n",
+    "spec = Spec.load()\n",
+    "\n",
+    "# Add all values to the validator.\n",
+    "spec_validator = SpecValidator(spec)\n",
+    "spec_validator.add_value(MultipleAccuracy.load(\"accuracy across gardens.value\"))\n",
+    "spec_validator.add_value(RankSums.load(\"ranksums blur2x8.value\"))\n",
+    "spec_validator.add_value(RankSums.load(\"ranksums blur5x8.value\"))\n",
+    "spec_validator.add_value(RankSums.load(\"ranksums blur0x8.value\"))\n",
+    "spec_validator.add_value(\n",
+    "    MultipleRanksums.load(\"multiple ranksums for clade2.value\")\n",
+    ")\n",
+    "spec_validator.add_value(\n",
+    "    MultipleRanksums.load(\"multiple ranksums between clade2 and 3.value\")\n",
+    ")\n",
+    "spec_validator.add_value(Integer.load(\"model size.value\"))\n",
+    "spec_validator.add_value(CPUStatistics.load(\"predicting cpu.value\"))\n",
+    "spec_validator.add_value(MemoryStatistics.load(\"predicting memory.value\"))\n",
+    "spec_validator.add_value(Image.load(\"image attributions.value\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Validate requirements and get validated details.\n",
+    "validated_spec = spec_validator.validate()\n",
+    "validated_spec.save(force=True)\n",
+    "\n",
+    "# We want to see the validation results in the Notebook, regardles sof them being saved.\n",
+    "validated_spec.print_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we see some of the results of the validation.\n",
+    "\n",
+    "For example, there is a significant difference between original model with no blur and blur 0x8. So we see a drop in model accuracy with increasing blur. But aside from max blur (0x8), the model accuracy fall off isn't bad.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate a Report\n",
+    "\n",
+    "The final step of SDMT involves the generation of a report to communicate the results of model evaluation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlte.model.shared import (\n",
+    "    ProblemType,\n",
+    "    GoalDescriptor,\n",
+    "    MetricDescriptor,\n",
+    "    ModelProductionDescriptor,\n",
+    "    ModelInterfaceDescriptor,\n",
+    "    ModelInputDescriptor,\n",
+    "    ModelOutputDescriptor,\n",
+    "    ModelResourcesDescriptor,\n",
+    "    RiskDescriptor,\n",
+    "    DataDescriptor,\n",
+    "    DataClassification,\n",
+    "    FieldDescriptor,\n",
+    "    LabelDescriptor,\n",
+    ")\n",
+    "from mlte.report.artifact import (\n",
+    "    Report,\n",
+    "    SummaryDescriptor,\n",
+    "    PerformanceDesciptor,\n",
+    "    IntendedUseDescriptor,\n",
+    "    CommentDescriptor,\n",
+    "    QuantitiveAnalysisDescriptor,\n",
+    ")\n",
+    "\n",
+    "report = Report(\n",
+    "    summary=SummaryDescriptor(\n",
+    "        problem_type=ProblemType.CLASSIFICATION, task=\"Flower classification\"\n",
+    "    ),\n",
+    "    performance=PerformanceDesciptor(\n",
+    "        goals=[\n",
+    "            GoalDescriptor(\n",
+    "                description=\"The model should perform well.\",\n",
+    "                metrics=[\n",
+    "                    MetricDescriptor(\n",
+    "                        description=\"accuracy\",\n",
+    "                        baseline=\"Better than random chance.\",\n",
+    "                    )\n",
+    "                ],\n",
+    "            )\n",
+    "        ]\n",
+    "    ),\n",
+    "    intended_use=IntendedUseDescriptor(\n",
+    "        usage_context=\"A handheld flower identification device.\",\n",
+    "        production_requirements=ModelProductionDescriptor(\n",
+    "            integration=\"integration\",\n",
+    "            interface=ModelInterfaceDescriptor(\n",
+    "                input=ModelInputDescriptor(description=\"Vector[150]\"),\n",
+    "                output=ModelOutputDescriptor(description=\"Vector[3]\"),\n",
+    "            ),\n",
+    "            resources=ModelResourcesDescriptor(\n",
+    "                cpu=\"1\", gpu=\"0\", memory=\"6MiB\", storage=\"2KiB\"\n",
+    "            ),\n",
+    "        ),\n",
+    "    ),\n",
+    "    risks=RiskDescriptor(\n",
+    "        fp=\"The wrong type of flower is identified.\",\n",
+    "        fn=\"The flower is not identified.\",\n",
+    "        other=\"N/A\",\n",
+    "    ),\n",
+    "    data=[\n",
+    "        DataDescriptor(\n",
+    "            description=\"Flower dataset.\",\n",
+    "            classification=DataClassification.UNCLASSIFIED,\n",
+    "            access=\"None\",\n",
+    "            fields=[\n",
+    "                FieldDescriptor(\n",
+    "                    name=\"Sepal length\",\n",
+    "                    description=\"The length of the sepal.\",\n",
+    "                    type=\"float\",\n",
+    "                    expected_values=\"N/A\",\n",
+    "                    missing_values=\"N/A\",\n",
+    "                    special_values=\"N/A\",\n",
+    "                )\n",
+    "            ],\n",
+    "            labels=[\n",
+    "                LabelDescriptor(description=\"Dahlia\", percentage=30.0),\n",
+    "                LabelDescriptor(description=\"Sunflower\", percentage=30.0),\n",
+    "                LabelDescriptor(description=\"Azalea\", percentage=40.0),\n",
+    "            ],\n",
+    "            policies=\"N/A\",\n",
+    "            rights=\"N/A\",\n",
+    "            source=\"https://www.robots.ox.ac.uk/~vgg/data/flowers/102/\",\n",
+    "            identifiable_information=\"N/A\",\n",
+    "        )\n",
+    "    ],\n",
+    "    comments=[\n",
+    "        CommentDescriptor(\n",
+    "            content=\"This model should not be used for nefarious purposes.\"\n",
+    "        )\n",
+    "    ],\n",
+    "    quantitative_analysis=QuantitiveAnalysisDescriptor(\n",
+    "        content=\"Insert graph here.\"\n",
+    "    ),\n",
+    "    validated_spec_id=validated_spec.identifier,\n",
+    ")\n",
+    "\n",
+    "report.save(force=True, parents=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "82adda432962015d5f71beb9387a99f24d390514e497c776c87ff3434daf7312"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/demo/scenarios/requirements.ipynb b/demo/scenarios/requirements.ipynb
new file mode 100644
index 000000000..4cea581f1
--- /dev/null
+++ b/demo/scenarios/requirements.ipynb
@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# QAS Demo\n",
+    "\n",
+    "This is a set of demo notebooks to illustrate the use of the MLTE library and SDMT process, using Quality Attribute Scenarios as guidance for the required Properties and Conditions.\n",
+    "\n",
+    "NOTE: this demo has an additional set of requirements than MLTE. You can install them from the file in this folder, with the command: \n",
+    "\n",
+    "`pip --default-timeout 1000 install -r requirements.txt`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 0. Quality Attribute Scenarios\n",
+    "\n",
+    "The following are the QASs that we want to validate through the use of MLTE. The examples below relate to a hypothetical system used by visitors to a botanical garden to identify flowers in the different gardens and learn more about them. The system used an ML model that was trained on the flower category dataset [Nilsback 2008] (https://www.robots.ox.ac.uk/~vgg/data/flowers/102/). \n",
+    "\n",
+    "* **Fairness - Model Impartial to Photo Location**\n",
+    "  * The model receives a picture taken at the garden and, regardless of the garden location, can correctly identify the correct flowers at least 90% of the time. Test data needs to include pictures of the flowers from the different gardens, grouped by the garden that the image was taken at. The quantity of the flower images should be representative of the garden population they are taken from. The total accuracy of the model across each garden population should be higher or equal to 0.9.\n",
+    "* **Robustness- Model Robust to Noise (Image Blur)**\n",
+    "  * The model receives a picture taken at a garden by a member of the general public, and it is a bit blurry.  The model should still be able to successfully identify the flower at the same rate as non-blurry images. Test data needs to include blurred flower images.  Blurred images will be created using ImageMagick. Three datasets will be generated, each with different amounts of blur: minimal blur, maximum blur, and in between minimal and maximum blur. Blurry images are successfully identified at rates equal to that of non-blurred images. This will be measured using the Wilcoxon Rank-Sum test, with significance at p-value <=0.05.\n",
+    "* **Robustness - Model Robust to Noise (Channel Loss)**\n",
+    "  * The model receives a picture taken at a garden using a loaned device. These devices are known to sometimes lose a channel (i.e., RGB channel). The model should still be able to successfully identify the flower at the same rate as full images. Test data needs to include images with a missing channel. Test images will be generated by removing the R, G and B channels in the original test data using ImageMagic, therefore producing three data sets. Images with a missing channel are successfully identified at rates equal to that of original images. This will be measured using the Wilcoxon Rank-Sum test, with significance at p-value <=0.05.\n",
+    "* **Performance on Operational Platform**\n",
+    "  * The model will need to run on the devices loaned out by the garden centers to visitors. These are small, inexpensive devices with limited CPU power, as well as limited memory and disk space (512 MB and 128 GB, respectively). The original test dataset can be used. 1- Executing the model on the loaned platform will not exceed maximum CPU usage of 30% to ensure reasonable response time. CPU usage will be measure using ps. 2- Memory usage at inference time will not exceed available memory of 512 MB. This will be measured using pmap. 3 - Disk usage will not exceed available disk space of 128 GB. This will be measured using by adding the size of each file in the path for the model code.\n",
+    "* **Interpretability - Understanding Model Results**\n",
+    "  * The application that runs on the loaned device should indicate the main features that were used to recognize the flower, as part of the educational experience. The app will display the image highlighting the most informative features in flower identification, in addition to the flower name. The original test data set can be used. The model needs to return evidence, in this case a heat map implementing the Integrated Gradients algorithm, showing the pixels that were most informative in the classification decision. This evidence should be returned with each inference. \n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Define a Specification\n",
+    "\n",
+    "In the first phase of SDMT, we define a `Specification` that represents the requirements the completed model must meet in order to be acceptable for use in the system into which it will be integrated."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Initialize MLTE Context\n",
+    "\n",
+    "MLTE contains a global context that manages the currently active _session_. Initializing the context tells MLTE how to store all of the artifacts that it produces."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from mlte.session import set_context, set_store\n",
+    "\n",
+    "store_path = os.path.join(os.getcwd(), \"store\")\n",
+    "os.makedirs(\n",
+    "    store_path, exist_ok=True\n",
+    ")  # Ensure we are creating the folder if it is not there.\n",
+    "\n",
+    "set_context(\"ns\", \"OxfordFlower\", \"0.0.1\")\n",
+    "set_store(f\"local://{store_path}\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Build a `Specification`\n",
+    "\n",
+    "In MLTE, we define requirements by constructing a specification (`Spec`). For each property, we define the validations to perform as well. Note that several new `Value` types (`MultipleAccuracy`, `RankSums`, `MultipleRanksums`) had to be created to define the validation methods that will validate each Condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlte.spec.spec import Spec\n",
+    "\n",
+    "# The Properties we want to validate, associated with our scenarios.\n",
+    "from mlte.property.costs.storage_cost import StorageCost\n",
+    "from properties.fairness import Fairness\n",
+    "from properties.robustness import Robustness\n",
+    "from properties.interpretability import Interpretability\n",
+    "from properties.predicting_memory_cost import PredictingMemoryCost\n",
+    "from properties.predicting_compute_cost import PredictingComputeCost\n",
+    "\n",
+    "# The Value types we will use to validate each condition.\n",
+    "from mlte.measurement.storage import LocalObjectSize\n",
+    "from mlte.measurement.cpu import LocalProcessCPUUtilization\n",
+    "from mlte.measurement.memory import LocalProcessMemoryConsumption\n",
+    "from mlte.value.types.image import Image\n",
+    "from values.multiple_accuracy import MultipleAccuracy\n",
+    "from values.ranksums import RankSums\n",
+    "from values.multiple_ranksums import MultipleRanksums\n",
+    "\n",
+    "# The full spec. Note that the Robustness Property contains conditions for both Robustness scenarios.\n",
+    "spec = Spec(\n",
+    "    properties={\n",
+    "        Fairness(\n",
+    "            \"Important check if model performs well accross different populations\"\n",
+    "        ): {\n",
+    "            \"accuracy across gardens\": MultipleAccuracy.all_accuracies_more_or_equal_than(\n",
+    "                0.9\n",
+    "            )\n",
+    "        },\n",
+    "        Robustness(\"Robust against blur and noise\"): {\n",
+    "            \"ranksums blur2x8\": RankSums.p_value_greater_or_equal_to(0.05 / 3),\n",
+    "            \"ranksums blur5x8\": RankSums.p_value_greater_or_equal_to(0.05 / 3),\n",
+    "            \"ranksums blur0x8\": RankSums.p_value_greater_or_equal_to(0.05 / 3),\n",
+    "            \"multiple ranksums for clade2\": MultipleRanksums.all_p_values_greater_or_equal_than(\n",
+    "                0.05\n",
+    "            ),\n",
+    "            \"multiple ranksums between clade2 and 3\": MultipleRanksums.all_p_values_greater_or_equal_than(\n",
+    "                0.05\n",
+    "            ),\n",
+    "        },\n",
+    "        StorageCost(\"Critical since model will be in an embedded device\"): {\n",
+    "            \"model size\": LocalObjectSize.value().less_than(3000)\n",
+    "        },\n",
+    "        PredictingMemoryCost(\n",
+    "            \"Useful to evaluate resources needed when predicting\"\n",
+    "        ): {\n",
+    "            \"predicting memory\": LocalProcessMemoryConsumption.value().average_consumption_less_than(\n",
+    "                512000.0\n",
+    "            )\n",
+    "        },\n",
+    "        PredictingComputeCost(\n",
+    "            \"Useful to evaluate resources needed when predicting\"\n",
+    "        ): {\n",
+    "            \"predicting cpu\": LocalProcessCPUUtilization.value().max_utilization_less_than(\n",
+    "                30.0\n",
+    "            )\n",
+    "        },\n",
+    "        Interpretability(\"Important to understand what the model is doing\"): {\n",
+    "            \"image attributions\": Image.ignore(\"Inspect the image.\")\n",
+    "        },\n",
+    "    }\n",
+    ")\n",
+    "spec.save(parents=True, force=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "82adda432962015d5f71beb9387a99f24d390514e497c776c87ff3434daf7312"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/demo/scenarios/requirements.txt b/demo/scenarios/requirements.txt
new file mode 100644
index 000000000..49275a7c8
--- /dev/null
+++ b/demo/scenarios/requirements.txt
@@ -0,0 +1,8 @@
+numpy==1.24.1
+pandas==1.5.3
+pandas-stubs==1.5.3.230321
+scikit-learn==1.2.1
+matplotlib==3.6.3
+ipykernel==6.21.2
+notebook==6.5.3
+tensorflow==2.14.0
diff --git a/demo/scenarios/values/array_value.py b/demo/scenarios/values/array_value.py
new file mode 100644
index 000000000..648b59d43
--- /dev/null
+++ b/demo/scenarios/values/array_value.py
@@ -0,0 +1,46 @@
+"""
+Implementation of Array value.
+"""
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+from mlte.evidence.metadata import EvidenceMetadata, Identifier
+from mlte.value.base import ValueBase
+from mlte.value.types.real import Real
+
+
+class Array(ValueBase):
+    def __init__(self, evidence_metadata: EvidenceMetadata, array: np.ndarray):
+        super().__init__(self, evidence_metadata)
+
+        self.array: np.ndarray = array
+        """Underlying values represented as numpy array."""
+
+    def serialize(self) -> dict[str, Any]:
+        return {"array": [val for val in self.array]}
+
+    @staticmethod
+    def deserialize(
+        evidence_metadata: EvidenceMetadata, json_: dict[str, Any]
+    ) -> Array:
+        return Array(evidence_metadata, np.asarray(json_["array"]))
+
+    def __str__(self) -> str:
+        return str(self.array)
+
+    def get_as_real(self, position: int) -> Real:
+        """Return a value from the given position, as a Real value type."""
+        if position >= len(self.array):
+            raise RuntimeError(
+                f"Position {position} is not in array of size {len(self.array)}"
+            )
+        return_value = Real(self.metadata, float(self.array[position]))
+
+        # Add suffix to id based on position.
+        return_value.metadata.identifier = Identifier(
+            name=f"{return_value.metadata.identifier}.{position}"
+        )
+        return return_value
diff --git a/demo/scenarios/values/multiple_accuracy.py b/demo/scenarios/values/multiple_accuracy.py
new file mode 100644
index 000000000..61df8a870
--- /dev/null
+++ b/demo/scenarios/values/multiple_accuracy.py
@@ -0,0 +1,29 @@
+"""
+Implementation of MultipleAccuracy value.
+"""
+from __future__ import annotations
+
+from values.array_value import Array
+
+from mlte.spec.condition import Condition
+from mlte.validation.result import Failure, Success
+
+
+class MultipleAccuracy(Array):
+    """An array with multiple accuracies."""
+
+    @classmethod
+    def all_accuracies_more_or_equal_than(cls, threshold: float) -> Condition:
+        """Checks if the accuracy for multiple populations is fair by checking if all of them are over the given threshold."""
+        condition: Condition = Condition(
+            "all_accuracies_more_than",
+            [threshold],
+            lambda value: Success(
+                f"All accuracies are equal to or over threshold {threshold}"
+            )
+            if sum(g >= threshold for g in value.array) == len(value.array)
+            else Failure(
+                f"One or more accuracies are below threshold {threshold}: {value.array}"
+            ),
+        )
+        return condition
diff --git a/demo/scenarios/values/multiple_ranksums.py b/demo/scenarios/values/multiple_ranksums.py
new file mode 100644
index 000000000..2e90bf995
--- /dev/null
+++ b/demo/scenarios/values/multiple_ranksums.py
@@ -0,0 +1,73 @@
+"""
+Implementation of MultipleRaknsums value.
+"""
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+from values.array_value import Array
+
+from mlte.evidence.metadata import EvidenceMetadata
+from mlte.spec.condition import Condition
+from mlte.validation.result import Failure, Success
+
+
+class MultipleRanksums(Array):
+    """An array with multiple ranksums."""
+
+    def __init__(
+        self,
+        evidence_metadata: EvidenceMetadata,
+        array: np.ndarray,
+        num_pops: int = 1,
+    ):
+        super().__init__(evidence_metadata, array)
+
+        self.num_pops: int = num_pops
+        """Number of populations or groups.."""
+
+    def serialize(self) -> dict[str, Any]:
+        doc: dict[str, Any] = super().serialize()
+        doc["num_pops"] = self.num_pops
+        return doc
+
+    @staticmethod
+    def deserialize(
+        evidence_metadata: EvidenceMetadata, json_: dict[str, Any]
+    ) -> MultipleRanksums:
+        return MultipleRanksums(
+            evidence_metadata, np.asarray(json_["array"]), json_["num_pops"]
+        )
+
+    @classmethod
+    def all_p_values_greater_or_equal_than(cls, threshold: float) -> Condition:
+        """Checks if the p-value for multiple ranksums is below given threshold."""
+        condition: Condition = Condition(
+            "all_p_values_greater_or_equal_than",
+            [threshold],
+            lambda value: Success(
+                f"All p-values are equal to or over threshold {value.get_total_p_value_threshold(threshold)}"
+            )
+            if len(value.get_low_p_values(threshold)) == 0
+            else Failure(
+                f"One or more p-values are below threshold {value.get_total_p_value_threshold(threshold)}: {value.get_low_p_values(threshold)}"
+            ),
+        )
+        return condition
+
+    def get_total_p_value_threshold(self, threshold: float) -> float:
+        return threshold / self.num_pops
+
+    def get_low_p_values(self, threshold: float):
+        """Generates a dict of all cases that didn't go over the threshold."""
+        low_cases = {}
+
+        ranksum: dict[str, list]
+        for ranksum in self.array:
+            id = next(iter(ranksum))
+            pval = ranksum[id][1]
+            if pval < self.get_total_p_value_threshold(threshold=threshold):
+                low_cases[id] = pval
+
+        return low_cases
diff --git a/demo/scenarios/values/ranksums.py b/demo/scenarios/values/ranksums.py
new file mode 100644
index 000000000..5bb5bc382
--- /dev/null
+++ b/demo/scenarios/values/ranksums.py
@@ -0,0 +1,28 @@
+"""
+Implementation of RankSums value.
+"""
+from __future__ import annotations
+
+from values.array_value import Array
+
+from mlte.spec.condition import Condition
+from mlte.validation.result import Failure, Success
+
+
+class RankSums(Array):
+    """A RankSums array is an array with the results of the ranksums function (stat on first pos, p-value on second)."""
+
+    @classmethod
+    def p_value_greater_or_equal_to(cls, threshold: float) -> Condition:
+        condition: Condition = Condition(
+            "p_value_greater_or_equal_to",
+            [threshold],
+            lambda value: Success(
+                f"P-Value {value.array[1]} is greater or equal to {threshold}"
+            )
+            if value.array[1] >= threshold
+            else Failure(
+                f"P-Value {value.array[1]} is less than threshold {threshold}"
+            ),
+        )
+        return condition
diff --git a/demo/confusion_matrix.py b/demo/simple/confusion_matrix.py
similarity index 100%
rename from demo/confusion_matrix.py
rename to demo/simple/confusion_matrix.py
diff --git a/demo/evidence.ipynb b/demo/simple/evidence.ipynb
similarity index 99%
rename from demo/evidence.ipynb
rename to demo/simple/evidence.ipynb
index 29d4bef24..e5eea511c 100644
--- a/demo/evidence.ipynb
+++ b/demo/simple/evidence.ipynb
@@ -429,7 +429,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.16"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/demo/negotiation.ipynb b/demo/simple/negotiation.ipynb
similarity index 94%
rename from demo/negotiation.ipynb
rename to demo/simple/negotiation.ipynb
index 82d06099c..45c5d4cc2 100644
--- a/demo/negotiation.ipynb
+++ b/demo/simple/negotiation.ipynb
@@ -6,7 +6,11 @@
    "source": [
     "## 0. Define a Negotiation Card\n",
     "\n",
-    "In the first phase of SDMT, we negotiate the requirements for our model and system. `mlte` provides an artifact that assists in this process - the `NegotiationCard`."
+    "In the first phase of SDMT, we negotiate the requirements for our model and system. `mlte` provides an artifact that assists in this process - the `NegotiationCard`.\n",
+    "\n",
+    "NOTE: this demo has an additional set of requirements than MLTE. You can install them from the file in this folder, with the command: \n",
+    "\n",
+    "`pip --default-timeout 1000 install -r requirements.txt`"
    ]
   },
   {
@@ -162,7 +166,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.16"
   },
   "orig_nbformat": 4
  },
diff --git a/demo/report.ipynb b/demo/simple/report.ipynb
similarity index 99%
rename from demo/report.ipynb
rename to demo/simple/report.ipynb
index 0ea8b6d68..e92c09632 100644
--- a/demo/report.ipynb
+++ b/demo/simple/report.ipynb
@@ -247,7 +247,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.16"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/demo/requirements.ipynb b/demo/simple/requirements.ipynb
similarity index 99%
rename from demo/requirements.ipynb
rename to demo/simple/requirements.ipynb
index bcbc67d6c..0d1817b0c 100644
--- a/demo/requirements.ipynb
+++ b/demo/simple/requirements.ipynb
@@ -115,7 +115,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.16"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/demo/requirements.txt b/demo/simple/requirements.txt
similarity index 78%
rename from demo/requirements.txt
rename to demo/simple/requirements.txt
index e9f7b9dd9..bfaa94767 100644
--- a/demo/requirements.txt
+++ b/demo/simple/requirements.txt
@@ -1,5 +1,6 @@
 numpy==1.24.1
 pandas==1.5.3
+pandas-stubs==1.5.3.230321
 scikit-learn==1.2.1
 matplotlib==3.6.3
 ipykernel==6.21.2
diff --git a/demo/train.py b/demo/simple/train.py
similarity index 100%
rename from demo/train.py
rename to demo/simple/train.py