Description

Introduction

Model training and hyperparameter tuning are critical steps in the machine learning lifecycle. Vertex AI offers powerful tools for training models using custom code or AutoML, with built-in support for distributed training, hardware acceleration, and hyperparameter tuning via Vertex Vizier. This module provides a step-by-step guide to training and optimizing ML models effectively using Vertex AI.

Prerequisites

• Active Google Cloud project with Vertex AI API enabled

• Intermediate Python and ML framework knowledge (TensorFlow, PyTorch, or Scikit-learn)

• Experience with Jupyter notebooks or Google Cloud Console

• Basic understanding of datasets, training, and evaluation

Table of Contents

1. Introduction to Training in Vertex AI
   1.1 Overview of Training Options (AutoML vs Custom)
   1.2 Vertex AI Training Infrastructure
   1.3 Supported ML Frameworks and Containers
   1.4 Comparison with Local and On-Premise Training

2. Preparing for Model Training
   2.1 Data Format and Storage in GCS
   2.2 Creating and Registering Datasets in Vertex AI
   2.3 Setting Up Training Scripts and Packages
   2.4 Custom Container vs Prebuilt Container

3. Running Custom Training Jobs
   3.1 Creating a Training Job from Console
   3.2 Using Vertex AI SDK for Job Submission
   3.3 Selecting Compute Resources (CPU/GPU/TPU)
   3.4 Monitoring Training Logs and Status

4. Hyperparameter Tuning with Vertex Vizier
   4.1 Introduction to Vertex AI Vizier
   4.2 Defining Search Space and Objective Metric
   4.3 Configuring Trials and Strategy (Grid, Random, Bayesian)
   4.4 Running Tuning Jobs and Analyzing Results

5. Distributed Training and Scaling
   5.1 When to Use Distributed Training
   5.2 Setting Up Multi-Worker Training Jobs
   5.3 Managing Resource Usage and Quotas
   5.4 Best Practices for Performance and Cost

6. Model Evaluation and Export
   6.1 Using Built-in Evaluation Metrics
   6.2 Visualizing Results in TensorBoard
   6.3 Exporting Trained Models to GCS
   6.4 Registering Models in Vertex AI Model Registry

7. Deployment Preparation
   7.1 Optimizing Models for Inference
   7.2 Exporting Custom Predict Functions
   7.3 Packaging Models for Deployment
   7.4 Versioning and Tagging Best Practices

8. Troubleshooting and Best Practices
   8.1 Debugging Failed Training Jobs
   8.2 IAM Permissions for Training Pipelines
   8.3 Using Logging and Stackdriver Integration
   8.4 Tips to Improve Model Accuracy and Reduce Cost

9. Real-World Training Scenarios
   9.1 Training NLP Models with Hugging Face Transformers
   9.2 Vision Models using TensorFlow on GPUs
   9.3 Tabular Models with Scikit-learn
   9.4 Hybrid Pipelines with AutoML and Custom Code

10. Advancing Further
    10.1 Combining Training with Pipelines
    10.2 Training Monitoring and Drift Detection
    10.3 CI/CD for ML Training Workflows
    10.4 Certifications and Learning Resources

Model training and tuning in Vertex AI provide a scalable, reliable, and efficient way to build production-grade machine learning solutions. By leveraging Vertex AI’s automated tools, custom training environments, and hyperparameter tuning capabilities, teams can build more accurate and optimized models faster.
Mastering these features is key to unlocking end-to-end ML workflows in modern cloud-native environments.