Once model development, evaluation, and tuning is complete, along with multiple iterations of improving the results, the final stage of model deployment comes into the picture. Model deployment takes care of aspects such as model persistence, exposing models to other applications through different mechanisms such as API endpoints, and so on, along with developing monitoring strategies.

We live in a dynamic world where everything changes every so often, and the same is true about data and other factors related to our use cases. It is imperative that we put in place monitoring strategies such as regular reports, logs, and tests to keep a check on the performance of our solutions and make changes as and when required.

ML pipelines are as much about software engineering as they are about data science and ML. We outlined and discussed the different components of a typical pipeline in brief. Depending upon specific use cases, we modify the standard pipeline to suit the needs yet make sure we do not overlook known pitfalls. In the coming sections, let's understand a couple of the components of a typical ML pipeline in a bit more detail, with actual examples and code snippets.