diff --git a/open-machine-learning-jupyter-book/machine-learning-productionization/data-engineering.ipynb b/open-machine-learning-jupyter-book/machine-learning-productionization/data-engineering.ipynb
index 4b122528c6..2f7dc3b178 100644
--- a/open-machine-learning-jupyter-book/machine-learning-productionization/data-engineering.ipynb
+++ b/open-machine-learning-jupyter-book/machine-learning-productionization/data-engineering.ipynb
@@ -80,7 +80,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Architecture: Ingestion, data Lake, preparation & computing, data warehouse, presentation"
+    "**Architecture: Ingestion, data Lake, preparation & computing, data warehouse, presentation**"
    ]
   },
   {
@@ -91,7 +91,7 @@
     "---\n",
     "name: Big Data Pipeline on AWS, Microsoft Azure, and Google Cloud\n",
     "---\n",
-    "[Big Data Pipeline on AWS, Microsoft Azure, and Google Cloud](https://www.reddit.\n",
+    "[Big Data Pipeline on AWS, Microsoft Azure, and Google Cloud](https://www.reddit.com/r/bigdata/comments/mkfsfi/big_data_pipeline_on_aws_microsoft_azure_and/)\n",
     ":::"
    ]
   },
@@ -99,7 +99,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Architecture: Capture, process, store, analyze, use"
+    "**Architecture: Capture, process, store, analyze, use**"
    ]
   },
   {
@@ -118,7 +118,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Ingestion, storage, cataloging & search, processing, consumption, security & governance"
+    "**Ingestion, storage, cataloging & search, processing, consumption, security & governance**"
    ]
   },
   {
@@ -373,22 +373,6 @@
    "source": [
     "Data cleaning is a key part of data engineering to improve the [data quality](#data-quality), but it can be deeply frustrating as the situation could be highly varied in different datasets. Sometimes you will see the text fields garbled. Sometimes your dates are formatted incorrectly. In this [assignment](../assignments/machine-learning-productionization/data-engineering), you’ll work through three hands-on exercises to deal with messy data."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bcb7aebc",
-   "metadata": {},
-   "source": [
-    "## Self study"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3870cf84",
-   "metadata": {},
-   "source": [
-    "- [A Chat with Andrew on MLOps: From Model-centric to Data-centric AI - YouTube](https://www.youtube.com/watch?v=06-AZXmwHjo)"
-   ]
   }
  ],
  "metadata": {