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
- Course introduction
- Understanding the ML Enterprise Workflow
- Introduction
- Overview of an ML enterprise workflow
- Quiz: Understanding the ML Enterprise Workflow
- Resources: Understanding the ML Enterprise Workflow
- Data in the Enterprise
- Introduction
- Feature Store
- Data Catalog
- Dataplex
- Analytics Hub
- Data preprocessing options
- Dataprep
- Lab intro: Exploring and Creating an Ecommerce Analytics Pipeline with Dataprep
- Exploring and Creating an Ecommerce Analytics Pipeline with Cloud Dataprep v1.5
- Quiz: Data in the Enterprise
- Resources: Data in the Enterprise
- Science of Machine Learning and Custom Training
- Introduction
- The art and science of machine learning
- Make training faster
- When to use custom training
- Training requirements and dependencies (part 1)
- Training requirements and dependencies (part 2)
- Training custom ML models using Vertex AI
- Vertex AI: Qwik Start
- Quiz: Science of Machine Learning and Custom Training
- Resources: Science of Machine Learning and Custom Training
- Resources: The Science of Machine Learning
- Vertex Vizier Hyperparameter Tuning
- Introduction
- Vertex AI Vizier hyperparameter tuning
- Lab intro: Vertex AI: Hyperparameter Tuning
- Vertex AI: Hyperparameter Tuning
- Quiz: Vertex Vizier Hyperparameter Tuning
- Resources: Vertex Vizier Hyperparameter Tuning
- Prediction and Model Monitoring Using Vertex AI
- Introduction
- Predictions using Vertex AI
- Model management using Vertex AI
- Lab intro: Monitoring Vertex AI Models
- Monitoring Vertex AI Models
- Quiz: Prediction and Model Monitoring Using Vertex AI
- Resources: Prediction and Model Monitoring Using Vertex AI
- Vertex AI Pipelines
- Introduction
- Prediction using Vertex AI pipelines
- Lab intro: Vertex AI Pipelines
- Lab Introduction and Walkthrough: Vertex AI pipeline
- Introduction to Vertex Pipelines
- Running Pipelines on Vertex AI 2.5
- Quiz: Vertex AI Pipelines
- Resources: Vertex AI Pipelines
- Best Practices for ML Development
- Introduction
- Best practices for model deployment and serving
- Best practices for model monitoring
- Vertex AI pipeline best practices
- Best practices for artifact organization
- Resources: Best Practices for ML Development on Vertex AI
- Course Summary
- Summary
- Resource: All quiz questions
- Resources: All readings
- Resource: All slides
- Series Summary
- Series summary
- Resource: Best practices summary
- Your Next Steps
- Course Badge
