diff --git a/workflows/train_mnist_wf/README.md b/workflows/train_mnist_wf/README.md
index 4539013..1ed944f 100644
--- a/workflows/train_mnist_wf/README.md
+++ b/workflows/train_mnist_wf/README.md
@@ -1,14 +1,28 @@
-## Change the following variable value before deploying the workflow on your workspace
+## There are few pre-requisite we have fulfill before deploying the workflow
 
-- Default value of `workspace_fqn` in main workflow function.
-- Value of env variables `TFY_API_KEY` `TFY_HOST` in `task_config`.
-- you can use virtual accout token as `TFY_API_KEY`, click [here](https://docs.truefoundry.com/docs/generating-truefoundry-api-keys#virtual-accounts) to learn about how to create virtual account.
-- `host` value in `deploy.py`
+### Creating the ml repo and giving the workspace the access to that ml repo
+- First create a ml repo where you want to log the models. To learn about how to create a ml repo, click [here](https://docs.truefoundry.com/docs/creating-a-ml-repo#/).
+- Give ml repo access to the workspace where you will be deploying your workflow and the model. To know about how to give access click [here](https://docs.truefoundry.com/docs/key-concepts#/grant-access-of-ml-repo-to-workspace)
+
+### Setting the value of default variables
+
+- Set the value of env variables `TFY_API_KEY` `TFY_HOST` in `task_config` in `train-deploy-workflow.py` file.
+- you can use virtual account token as `TFY_API_KEY`, click [here](https://docs.truefoundry.com/docs/generating-truefoundry-api-keys#virtual-accounts) to learn about how to create a virtual account.
+- `host` value in `Port` field in `deploy.py` file
 
 ## Deploying the workflow
 
 You can deploy the workflow using the following command, make sure your truefoudry cli version is more thatn `4.0.0`.
 
 ```bash
-tfy deploy workflow --name <wf-name> --file <file-name> --workspace-fqn <workspace-fqn>
-```
\ No newline at end of file
+tfy deploy workflow --name <wf-name> --file train-deploy-workflow.py --workspace-fqn <workspace-fqn>
+```
+**Make sure you have workflow helm chart installed in the workspace in which you are deploying workflow**
+
+## Executing the workflow
+The workflow takes following arguments as input while executing the workflow.
+`ml_repo`: The name of the ml repo where you want to deploy the model. The workspace should have access to this ml repo.
+`workspace_fqn`: Workspace fqn where you want to deploy the model.
+`epochs`: An array of integer which define the number of epoch you want to train the model for, each epoch will run with corresponding learning rate which you will give in `learning_rate` argument. The lenght of `epochs` and `learning_rate` shoud be same.
+`learning_rate`: An array of float where each number is the learning rate you want your model to train with, corresponding to the epochs defined at same postion.
+`accuracy_threshold`: The threshold value, so the workflow will deploy the model if its validation accuracy is greater than this threshold accuracy.
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diff --git a/workflows/train_mnist_wf/train-deploy-workflow.py b/workflows/train_mnist_wf/train-deploy-workflow.py
index 3c4e593..018f04c 100644
--- a/workflows/train_mnist_wf/train-deploy-workflow.py
+++ b/workflows/train_mnist_wf/train-deploy-workflow.py
@@ -15,6 +15,8 @@
     resources=Resources(cpu_request=1.2, cpu_limit=1.2, memory_limit=3000, memory_request=3000, ephemeral_storage_limit=2000, ephemeral_storage_request=2000),
     service_account="default",
     env={
+        "TF_CPP_MIN_LOG_LEVEL": "3", # suppress tensorflow warnings
+        "FLYTE_SDK_LOGGING_LEVEL": "40",
         "TFY_API_KEY": "<your-api-key>",
         "TFY_HOST": "<tfy-host-value>", 
         }
@@ -56,6 +58,7 @@ def train_model(epochs: int, learning_rate: float, data: Dict[str, np.array], ml
     model.compile(optimizer=optimizer, loss="sparse_categorical_crossentropy", metrics=["accuracy"])
 
     epochs = epochs
+    print(f"Started training the model for {epochs} epochs")
     history = model.fit(x_train, y_train, epochs=epochs, validation_data=(x_test, y_test))
 
     # Evaluate the model
@@ -122,11 +125,11 @@ def model_not_found(threshold: float) -> str:
 
 
 @workflow
-def model_training_workflow(ml_repo: str, epochs: List[int] = [2, 3, 5], learning_rate: List[float] = [0.1, 0.001, 0.001], accuracy_threshold: float = 0.15, workspace_fqn: str = "<your-ws-fqn>") -> Union[str, None]:
+def model_training_workflow(ml_repo: str, workspace_fqn: str, epochs: List[int] = [2, 3, 5], learning_rate: List[float] = [0.1, 0.001, 0.001], accuracy_threshold: float = 0.15) -> Union[str, None]:
     data = fetch_data()
     train_model_function = partial(train_model, data=data, ml_repo=ml_repo)
     fqns = map_task(train_model_function, concurrency=2)(epochs=epochs, learning_rate=learning_rate)
-    best_fqn, is_best_model_found = get_run_fqn_of_best_model(fqns=fqns, threshold=accuracy_threshold)
-    message = conditional("Deploy best model").if_(is_best_model_found == True).then(deploy_model(run_fqn=best_fqn, workspace_fqn=workspace_fqn)).else_().then(model_not_found(threshold=accuracy_threshold))
+    model_version_fqn, does_model_pass_threshold_accuracy = get_run_fqn_of_best_model(fqns=fqns, threshold=accuracy_threshold)
+    message = conditional("Deploy model").if_(does_model_pass_threshold_accuracy == True).then(deploy_model(run_fqn=model_version_fqn, workspace_fqn=workspace_fqn)).else_().then(model_not_found(threshold=accuracy_threshold))
 
     return message
\ No newline at end of file