In this chapter, we will cover how you can operationalize your MLFlow models using AWS SageMaker. We will cover how you can upload your runs to S3 storage, how you can build and push an MLFlow Docker container image to AWS, and how you can deploy your model, query it, update the model once it is deployed, and remove a deployed model.
Introduction
In the previous chapter, you learned what MLFlow is and how you can utilize the functionality it provides to integrate MLOps principles into your code. You also looked at how to deploy ...
