Machine Learning in the Enterprise - Locales

"This course, Machine Learning in the Enterprise - Locales, is intended for non-English learners. If you want to take this course in English, please enroll inMachine Learning in the Enterprise". This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks. The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to exporting a trained model. You will build a custom training machine learning model, which allows you to build a container image with little knowledge of Docker. #comment The case study team examines hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance. To understand more about model improvement, we dive into a bit of theory: we discuss regularization, dealing with sparsity, and many other essential concepts and principles. We end with an overview of prediction and model monitoring and how Vertex AI can be used to manage ML models.

• Introduction
• Course introduction
• Understanding the ML Enterprise Workflow
• Introduction
• Overview of an ML enterprise workflow
• Quiz: Understanding the ML Enterprise Workflow
• Data in the Enterprise
• Introduction
• Feature Store
• Data Catalog
• Dataplex
• Analytics Hub
• Data preprocessing options
• Preprocessing and transformation wtih 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
• 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
• Lab intro: Vertex AI: Custom Training Job and Prediction Using Managed Datasets
• Vertex AI: Custom Training Job and Prediction Using Managed Datasets
• Quiz: 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 Vizier Hyperparameter Tuning
• Vertex AI: Hyperparameter Tuning
• Using Vertex Vizier to Optimize Multiple Objectives
• Vertex Vizier Hyperparameter Tuning
• Prediction and Model Monitoring Using Vertex AI
• Introduction
• Predictions using Vertex AI
• Vertex SDK: Custom Training Tabular Regression Models for Online Prediction and Explainability
• Model management using Vertex AI
• Lab intro: Vertex AI Model Monitoring
• Monitoring Vertex AI Models
• Quiz: 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
• Create and Run ML Pipelines with Vertex Pipelines
• Quiz: 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
• Course Summary
• Summary
• Resource: All quiz questions
• Resources: All readings
• Resource: All slides
• Series Summary
• Series summary
• Resource: Best practices summary