Description

Introduction

Vertex AI supports custom model training using popular deep learning frameworks like TensorFlow and PyTorch. With scalable infrastructure, managed notebooks, and orchestration tools, developers can efficiently train, tune, and deploy models in a production-ready environment without managing servers or infrastructure manually.

Prerequisites

• Basic knowledge of TensorFlow and/or PyTorch

• Familiarity with training loops and model architectures

• Google Cloud project with Vertex AI enabled

• IAM roles: Vertex AI Admin, Storage Admin

Table of Contents

1. Overview of Custom Training in Vertex AI
   1.1 Why Use Vertex AI for Deep Learning Workflows
   1.2 TensorFlow & PyTorch Support in Vertex AI
   1.3 Managed Infrastructure and Custom Containers
   1.4 Pricing and Compute Options

2. Preparing Your Model
   2.1 Writing Custom TensorFlow and PyTorch Code
   2.2 Using Pretrained Models and Fine-tuning
   2.3 Structuring the Training Code for Vertex AI
   2.4 Saving Model Artifacts for Deployment

3. Using Vertex AI Workbench
   3.1 Creating and Using Managed Notebooks
   3.2 Installing TensorFlow and PyTorch in Notebooks
   3.3 Accessing Datasets from Cloud Storage or BigQuery
   3.4 Experiment Tracking in Jupyter Environments

4. Training with Custom Containers
   4.1 Creating Docker Images with Training Scripts
   4.2 Using Prebuilt TensorFlow and PyTorch Containers
   4.3 Storing Images in Artifact Registry
   4.4 Submitting Custom Training Jobs

5. Training with Custom Python Packages
   5.1 Writing a Trainer Script with Setup.py
   5.2 Uploading to Cloud Storage and Submitting Jobs
   5.3 Configuring Compute Resources and GPUs
   5.4 Debugging and Logging Training Jobs

6. Hyperparameter Tuning
   6.1 Defining Hyperparameter Ranges
   6.2 Running Trials in Vertex AI
   6.3 Early Stopping and Goal Metrics
   6.4 Analyzing Results and Selecting the Best Model

7. Model Evaluation and Deployment
   7.1 Exporting SavedModel or TorchScript Format
   7.2 Registering Models in Vertex AI Model Registry
   7.3 Deploying Models to Endpoints
   7.4 Real-Time vs Batch Prediction Options

8. Performance Optimization
   8.1 Using TPUs for TensorFlow Jobs
   8.2 Distributed Training with Multi-Worker Strategies
   8.3 GPU Resource Scaling and Cost Management
   8.4 Model Quantization and Compression Techniques

9. CI/CD Integration
   9.1 Automating Model Builds with Cloud Build
   9.2 Using Vertex AI Pipelines for Training Workflows
   9.3 Managing Versioning and Rollbacks
   9.4 GitOps and Continuous Deployment for ML Models

10. Monitoring and Maintenance
    10.1 Monitoring Prediction Quality
    10.2 Drift Detection and Retraining
    10.3 Logs, Alerts, and Audit Trails
    10.4 Updating and Decommissioning Models

Vertex AI offers a robust, scalable platform for training and deploying TensorFlow and PyTorch models in production.
By leveraging custom containers, hyperparameter tuning, and managed endpoints, developers can build powerful, reproducible, and efficient ML workflows end to end.