This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
Overview
Syllabus
- Introduction
- This module provides an overview of the course and its objectives.
- Understanding the ML Enterprise Workflow
- This module discusses the ML enterprise workflow and the purpose of each step.
- Data in the Enterprise
- This module reviews Google’s enterprise data management and governance tools: Feature Store, Data Catalog, Dataplex, and Analytics Hub.
- Science of Machine Learning and Custom Training
- This module reviews the art and science of machine learning and neural networks. We'll also discuss how to train custom ML models using Vertex AI.
- Vertex Vizier Hyperparameter Tuning
- In this module we discuss how to do hyperparameter tuning using Vertex AI Vizier.
- Prediction and Model Monitoring Using Vertex AI
- This module covers Vertex AI prediction and model monitoring. We'll first discuss batch and online predictions using pre-built and custom containers, then we'll review model monitoring, which is a service that helps manage the performance of your ML models.
- Vertex AI Pipelines
- This module discusses Vertex AI pipelines and how to build them to orchestrate your ML workflow.
- Best Practices for ML Development
- This module reviews best practices for a number of different machine learning processes in Vertex AI.
- Course Summary
- This module is a summary of the Machine Learning in the Enterprise course.
- Series Summary
- This module is a summary of the Machine Learning on Google Cloud course series.
Taught by
Google Cloud Training
