diff --git a/docs/getting_started.md b/docs/getting_started.md
index c89ae6d7..86eb73df 100644
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@@ -151,16 +151,29 @@ Also check the published training pipeline in the **mlops-AML-WS** workspace in
 
 Great, you now have the build pipeline set up which automatically triggers every time there's a change in the master branch!
 
-* The first stage of the pipeline, **Model CI**, does linting, unit testing, code coverage, building, and publishes an **ML Training Pipeline** in an **ML Workspace**.
+The pipeline stages are summarized below:
 
-* The second stage of the pipeline, **Train model**, triggers the run of the Azure ML training pipeline. The training pipeline will train, evaluate, and register a new model. The actual computation happens on an [Azure Machine Learning Compute cluster](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute). In Azure DevOps, the stage runs an agentless job that waits for the completion of the Azure ML job. This allows the pipeline to wait for training completion for hours or even days without using agent resources.
+#### Model CI
 
-* **Note:** If the model evaluation determines that the new model doesn't perform any better than the previous one, the new model won't register and the pipeline will be **canceled**.
-  * In this case, you'll see a message in the 'Train Model' job under the 'Determine if evaluation succeeded and new model is registered' step saying '**Model was not registered for this run.**'
-  * See [evaluate_model.py](../diabetes_regression/evaluate/evaluate_model.py#L118) for the evaluation logic and [diabetes_regression_verify_train_pipeline.py](../ml_service/pipelines/diabetes_regression_verify_train_pipeline.py#L54) for the pipeline reporting logic.
-  * [Additional Variables and Configuration](#additional-variables-and-configuration) for configuring this and other behavior.
+- Linting (code quality analysis)
+- Unit tests and code coverage analysis
+- Build and publish *ML Training Pipeline* in an *ML Workspace*
 
-* The third stage of the pipeline, **Deploy to ACI**, deploys the model to the QA environment in [Azure Container Instances](https://azure.microsoft.com/en-us/services/container-instances/). After deployment, it runs a *smoke test* for validation. The test sends a sample query to the scoring web service and verifies that it returns the expected response. Have a look at the [smoke test code](../ml_service/util/smoke_test_scoring_service.py) for an example.
+#### Train model
+
+- Determine the ID of the *ML Training Pipeline* published in the previous stage.
+- Trigger the *ML Training Pipeline* and waits for it to complete.
+  - This is an **agentless** job. The CI pipeline can wait for ML pipeline completion for hours or even days without using agent resources.
+- Determine if a new model was registered by the *ML Training Pipeline*.
+  - If the model evaluation determines that the new model doesn't perform any better than the previous one, the new model won't register and the *ML Training Pipeline* will be **canceled**. In this case, you'll see a message in the 'Train Model' job under the 'Determine if evaluation succeeded and new model is registered' step saying '**Model was not registered for this run.**'
+  - See [evaluate_model.py](../diabetes_regression/evaluate/evaluate_model.py#L118) for the evaluation logic and [diabetes_regression_verify_train_pipeline.py](../ml_service/pipelines/diabetes_regression_verify_train_pipeline.py#L54) for the ML pipeline reporting logic.
+  - [Additional Variables and Configuration](#additional-variables-and-configuration) for configuring this and other behavior.
+
+#### Deploy to ACI
+
+- Deploy the model to the QA environment in [Azure Container Instances](https://azure.microsoft.com/en-us/services/container-instances/).
+- Smoke test
+  - The test sends a sample query to the scoring web service and verifies that it returns the expected response. Have a look at the [smoke test code](../ml_service/util/smoke_test_scoring_service.py) for an example.
 
 The pipeline uses a Docker container on the Azure Pipelines agents to accomplish the pipeline steps. The container image ***mcr.microsoft.com/mlops/python:latest*** is built with [this Dockerfile](../environment_setup/Dockerfile) and has all the necessary dependencies installed for MLOpsPython and ***diabetes_regression***. This image is an example of a custom Docker image with a pre-baked environment. The environment is guaranteed to be the same on any building agent, VM, or local machine. In your project, you'll want to build your own Docker image that only contains the dependencies and tools required for your use case. Your image will probably be smaller and faster, and it will be maintained by your team.