Update docs (#339)

* Update getting_started.md

* Update custom_model.md

docs/custom_model.md

@@ -80,7 +80,7 @@ To disable the evaluation step, either:
 
 ## Customize the build agent environment
 
-The DevOps pipeline definitions in the MLOpsPython template run several steps in a Docker container that contains the dependencies required to work through the Getting Started guide. If additional dependencies are required to run your unit tests or generate your Azure ML pipeline, there are a few options:
+The DevOps pipeline definitions in the MLOpsPython template run several steps in a Docker container that contains the dependencies required to work through the Getting Started guide. These dependencies may change over time and may not suit your project's needs. To manage your own dependencies, there are a few options:
 
 * Add a pipeline step to install dependencies required by unit tests to `.pipelines/code-quality-template.yml`. Recommended if you only have a small number of test dependencies.
 * Create a new Docker image containing your dependencies. See [docs/custom_container.md](custom_container.md). Recommended if you have a larger number of dependencies, or if the overhead of installing additional dependencies on each run is too high.

docs/getting_started.md

@@ -389,6 +389,7 @@ To remove the resources created for this project, use the [/environment_setup/ia
 ## Next Steps: Integrating your project
 
 - The [custom model](custom_model.md) guide includes information on bringing your own code to this repository template.
+- We recommend using a [custom container](custom_model.md#customize-the-build-agent-environment) to manage your pipeline environment and dependencies. The container provided with the getting started guide may not be suitable or up to date with your project needs.
 - Consider using [Azure Pipelines self-hosted agents](https://docs.microsoft.com/en-us/azure/devops/pipelines/agents/agents?view=azure-devops&tabs=browser#install) to speed up your Azure ML pipeline execution. The Docker container image for the Azure ML pipeline is sizable, and having it cached on the agent between runs can trim several minutes from your runs.
 
 ### Additional Variables and Configuration