End-to-end machine learning lifecycle

TLDR

A machine learning (ML) project requires collaboration across multiple roles in a business. We’ll introduce the high level steps of what the end-to-end ML lifecycle looks like and how different roles can collaborate to complete the ML project.

Outline

Introduction
Define problem
Collect data
Prepare data
Train, evaluate, and improve model
Deploy and integrate model
Monitor model
Conclusion

Introduction

Machine learning is a powerful tool to help solve different problems in your business. The article “Building your first machine learning model” gives you basic ideas of what it takes to build a machine learning model. In this article, we’ll talk about what the end-to-end machine learning project lifecycle looks like in a real business. The chart below shows the high level steps from project initiation to completion. Completing a ML project requires collaboration across multiple roles, including product manager, product developer, data scientist, and MLOps engineer. Failing to accurately execute on any one of these steps will result in misleading insights or models with no practical value.