Developing your machine learning baseline pipeline

For our machine learning platform, we will start with a very simple, heuristic-based pipeline, in order to get the infrastructure of your end-to-end system working correctly and an environment where the machine learning models can iterate on it.

Important note

It is critical that the technical requirements are correctly installed in your local machine to follow along. The assumption on this section is that you have MLflow and Docker installed as per the Technical requirements section.

By the end of this section, you will be able to create our baseline pipeline. The baseline pipeline value is to enable rapid iteration to the model developers. So, basically, an end-to-end infrastructure with placeholders for training and model serving will be made available to the development team. Since it's all implemented in MLflow, it becomes easy to have specialization and focus of the different types of teams involved in a machine...