Course
Skills
Machine Learning in the Enterprise
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.
What you'll learn
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.
Table of contents
Introduction
1min
Introduction
1min
Understanding the ML Enterprise Workflow
6mins
Understanding the ML Enterprise Workflow
6mins
Data in the Enterprise
33mins
Data in the Enterprise
29mins
- Introduction 0m
- Feature Store 7m
- Data Catalog 3m
- Dataplex 5m
- Analytics Hub 4m
- Data preprocessing options 3m
- Dataprep 6m
- Lab intro: Exploring and Creating an Ecommerce Analytics Pipeline with Dataprep 0m
- Lab: Exploring and Creating an Ecommerce Analytics Pipeline with Cloud Dataprep v1.5 0m
- Resources: Data in the Enterprise 0m
Science of Machine Learning and Custom Training
36mins
- Introduction 1m
- The art and science of machine learning 7m
- Make training faster 8m
- When to use custom training 5m
- Training requirements and dependencies (part 1) 9m
- Training requirements and dependencies (part 2) 4m
- Training custom ML models using Vertex AI 2m
- Lab: Vertex AI: Custom Training Job and Prediction Using Managed Datasets 0m
- Lab intro: Vertex AI: Custom Training Job and Prediction Using Managed Datasets 0m
- Resources: Science of Machine Learning and Custom Training 0m
- Resources: The Science of Machine Learning 0m
Science of Machine Learning and Custom Training
36mins
- Introduction 1m
- The art and science of machine learning 7m
- Make training faster 8m
- When to use custom training 5m
- Training requirements and dependencies (part 1) 9m
- Training requirements and dependencies (part 2) 4m
- Training custom ML models using Vertex AI 2m
- Lab: Vertex AI Workbench Notebook: Qwik Start 0m
- Resources: Science of Machine Learning and Custom Training 0m
- Resources: The Science of Machine Learning 0m
Vertex Vizier Hyperparameter Tuning
17mins
Vertex Vizier Hyperparameter Tuning
17mins
Prediction and Model Monitoring Using Vertex AI
16mins
Prediction and Model Monitoring Using Vertex AI
16mins
Vertex AI Pipelines
5mins
Vertex AI Pipelines
5mins
Best Practices for ML Development
11mins
Best Practices for ML Development
11mins
Course Summary
0mins
Course Summary
0mins
Series Summary
3mins
Series Summary
3mins
Introduction
0mins
Understanding the ML Enterprise Workflow
0mins
Data in the Enterprise
0mins
Science of Machine Learning and Custom Training
0mins
Vertex Vizier Hyperparameter Tuning
0mins
Prediction and Model Monitoring Using Vertex AI
0mins
Vertex AI Pipelines
0mins
Best Practices for ML Development
0mins
Course Summary
0mins
Series Summary
0mins
