Managing ML projects

Managing ML Projects shows you how to manage an ML project as it progresses from an idea to a production-ready implementation. The course covers the ML development phases and the roles and skills typically found on ML teams. It discusses strategies for working with stakeholders and provides details on how to plan and manage an ML project at each phase of development.

By demystifying the complexities inherent in ML projects, the course provides a solid theoretical framework for managing ML projects.

The course focuses on traditional ML models. Although generative AI is in the spotlight, traditional ML plays a vital role at Google, underpinning many services and projects, from predicting travel times in Maps to estimating the price of airline tickets in Flights, from predicting compute quota for Google Cloud customers to recommending relevant videos in YouTube.

In general, the principles for managing traditional ML projects are identical for managing generative AI projects. When there's a significant difference, the course provides relevant generative AI advice and guidance.

Estimated Course Length: 90 minutes

Objectives:

• Define the phases and elements of an ML project.
• Describe how to plan and manage an ML project.
• Determine business and model success metrics.
• Recognize the iterative process of running ML experiments.
• Design a solution for productionizing ML pipelines.
• Implement responsible ML and AI practices at each development phase.

Prerequisites:

• You should have a basic understanding of machine learning. For a brief introduction to machine learning concepts, see Introduction to Machine Learning. For a hands-on introduction to machine learning, see Machine Learning Crash Course.
• You should first verify that ML is the right approach for your problem. If you haven't framed your problem in terms of an ML solution, complete Introduction to Machine Learning Problem Framing.