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.
