Summary

In this chapter, you have gained a better understanding of ML engineering and how it differs from data science. You have also learned about some of the responsibilities of ML engineers. You must take note that the definition of ML engineering and the role of ML engineers are still evolving, as more and more techniques are surfacing. One such technique that we will not talk about in this book is online ML.

You have also learned how to create a custom notebook image and use it to standardize notebook environments. You have trained a model in the Jupyter notebook while using MLflow to track and compare your model development parameters, training results, and metrics. You have also seen how MLflow can be used as a model registry and how to promote model versions to different stages of the lifecycle.

The next chapter will continue the ML engineering domain and you will package and deploy ML models to be consumed as an API. You will then automate the package and deploy the...