Chapter 6: Machine Learning Engineering
In this chapter, we will move the discussion to the model building and model management activities of the machine learning (ML) engineering lifecycle. You will learn about the ML platform's role of providing a self-serving solution to data scientist so they can work more efficiently and collaborate with data teams and fellow data scientists.
The focus of this chapter is not on building models; instead, it is on showing how the platform can bring consistency and security across different environments and different members of your teams. You will learn how the platform simplifies the work of data scientists in terms of preparing and maintaining their data science workspaces.
In this chapter, you will learn about the following topics:
- Understanding ML engineering?
- Using a custom notebook image
- Introducing MLflow
- Using MLflow as an experiment tracking system
- Using MLflow as a model registry system
