Summary
In this chapter, we went through the theory of ML pipelines and practiced them by building ML pipelines for a business problem. We set up tools, resources, and the development environment for training these ML models. We started with the data ingestion step, followed by the model training step, testing step, and packaging step, and finally, we completed the registering step. Congrats! So far, you have implemented a critical building block of the MLOps workflow.
In the next chapter, we will look into evaluating and packaging production models.
