In our final piece in this series, we explore the scoping stage, where you define the project’s goals, assess feasibility, and determine the resources required to complete it successfully.

In Part 3 of “Machine Learning Lifecycle,” the author explores the nuts and bolts of data collection, from defining data sets to data splits.

In Part 2 of “Machine Learning Lifecycle,” the author explores selecting and training models, with data-centric model development at its core.

In this multi-part series, the author begins at the final stage of the machine learning lifecycle: deployment. Explore key challenges, deployment patterns, and degrees of automation.