How Google does Machine Learning

This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.

• Introduction to Course and Series
• This module introduces the course series and the Google experts who will be teaching it.
• What It Means to be AI-First
• In this module, you explore building a data strategy around machine learning.
• How Google Does ML
• This module shares the organizational know-how Google has acquired over the years.
• Machine Learning Development with Vertex AI
• All machine learning starts with some type of goal - whether it be a business use case, academic use case, or goal you are trying to solve. This module reviews the process of determining whether the model is ready for production the “proof of concept” or “experimentation” phase.
• Machine Learning Development with Vertex Notebooks
• This module explores both managed notebooks and user-managed notebooks for machine learning development in Vertex AI.
• Best Practices for Implementing Machine Learning on Vertex AI
• This module reviews best practices for a number of different machine learning processes in Vertex AI.
• Responsible AI Development
• This module discusses why machine learning systems aren’t fair by default and some of the things you have to keep in mind as you infuse ML into your products.
• Summary
• This module is a summary of the How Google Does Machine Learning course.