Description

Introduction
Vertex AI is a powerful platform on Google Cloud designed to streamline the process of building, training, and deploying machine learning models. One of its standout features is the ability to train custom models, coupled with advanced hyperparameter tuning capabilities, allowing data scientists and machine learning engineers to fine-tune models for optimal performance. In this course, you will learn how to utilize Vertex AI to build and train custom machine learning models, perform hyperparameter tuning, and improve model accuracy. You will gain hands-on experience with the Vertex AI workbench, training pipelines, and other tools that simplify the end-to-end machine learning lifecycle.

Prerequisites

• Basic understanding of machine learning concepts and algorithms
• Familiarity with Google Cloud Platform (GCP)
• Experience with Python and machine learning libraries such as TensorFlow or scikit-learn
• Knowledge of cloud computing and services like Google Cloud Storage and Google Compute Engine

Table of Contents

1. Introduction to Vertex AI
   1.1 Overview of Vertex AI and its Features
   1.2 Components of Vertex AI: Workbench, Pipelines, and Datasets
   1.3 Vertex AI in the ML Lifecycle
   1.4 Benefits of Using Vertex AI for Custom Model Training
2. Setting Up Vertex AI for Custom Model Training
   2.1 Setting Up a Google Cloud Project for Vertex AI
   2.2 Enabling Vertex AI API and Required Services
   2.3 Creating and Managing Datasets in Vertex AI
   2.4 Preparing Data for Custom Model Training
3. Building Custom Models in Vertex AI
   3.1 Creating a Training Job in Vertex AI
   3.2 Choosing the Right Framework for Custom Model Training (TensorFlow, PyTorch, etc.)
   3.3 Configuring Custom Training Scripts
   3.4 Submitting Training Jobs and Monitoring Progress
   3.5 Viewing and Analyzing Training Results
4. Hyperparameter Tuning with Vertex AI
   4.1 Understanding Hyperparameters and Their Impact on Model Performance
   4.2 Introduction to Vertex AI Hyperparameter Tuning
   4.3 Setting Up Hyperparameter Tuning Jobs
   4.4 Choosing Tuning Parameters and Ranges
   4.5 Running and Monitoring Hyperparameter Tuning Jobs
   4.6 Analyzing Tuning Results and Selecting the Best Model
5. Managing Training Resources
   5.1 Using AI Platform Notebooks for Model Development
   5.2 Scaling Compute Resources for Large-Scale Training Jobs
   5.3 Configuring GPUs and TPUs for Faster Training(Ref: Deploying and Serving Machine Learning Models with Vertex AI )
   5.4 Managing Storage and Data Access During Training
6. Model Evaluation and Validation
   6.1 Evaluating Model Performance with Test Data
   6.2 Using Vertex AI Model Evaluation Tools
   6.3 Assessing Metrics: Accuracy, Precision, Recall, and F1 Score
   6.4 Understanding and Addressing Overfitting and Underfitting
   6.5 Comparing Multiple Models and Hyperparameter Configurations
7. Deploying Custom Models with Vertex AI
   7.1 Introduction to Model Deployment in Vertex AI
   7.2 Deploying Models for Batch Prediction
   7.3 Real-Time Model Deployment and Serving
   7.4 Managing Endpoints and Monitoring Model Performance in Production
8. Integrating Vertex AI with Other Google Cloud Services
   8.1 Integrating with BigQuery for Data Storage and Analysis
   8.2 Using Cloud Functions for Event-Driven Model Execution
   8.3 Leveraging Google Cloud Storage for Dataset Management
   8.4 Automating Pipelines with Vertex AI Pipelines
9. Best Practices for Custom Model Training and Hyperparameter Tuning
   9.1 Selecting the Right Hyperparameters for Your Model
   9.2 Optimizing Resource Allocation for Training Jobs
   9.3 Monitoring and Debugging Training Jobs
   9.4 Using Vertex AI AutoML for Simplified Model Training
   9.5 Ensuring Scalability and Flexibility for Production-Ready Models
10. Advanced Topics in Custom Model Training and Hyperparameter Tuning
    10.1 Using Custom Containers for Training Jobs
    10.2 Distributed Training with Vertex AI
    10.3 Implementing Automated ML Pipelines for Continuous Improvement
    10.4 Hyperparameter Optimization with Bayesian Optimization and Other Techniques
11. Hands-On Projects and Real-World Scenarios
    11.1 Building a Custom Model for Image Classification
    11.2 Hyperparameter Tuning for Text Classification Models
    11.3 Deploying a Custom NLP Model for Production
    11.4 Monitoring and Managing Custom Model Performance in Real-Time

Conclusion
In this course, you will have learned how to leverage Vertex AI to build, train, and tune custom machine learning models. The ability to utilize Vertex AI’s powerful tools for custom model training and hyperparameter tuning allows you to optimize your machine learning workflows and deliver better performing models. By combining custom model training with intelligent tuning, you will ensure that your models are efficient, accurate, and scalable. Whether you’re building models for classification, regression, or complex deep learning tasks, Vertex AI provides the resources and capabilities needed for end-to-end machine learning success.

Reference