Introduction
Vertex AI is Google Cloud’s unified machine learning platform that simplifies the development, deployment, and maintenance of ML models. This course introduces you to the fundamentals of Vertex AI, equipping you with the knowledge to build, train, and deploy ML models on Google Cloud. Whether you’re a data scientist, machine learning engineer, or developer, this course provides a hands-on understanding of how Vertex AI accelerates machine learning workflows. You’ll explore its key features, such as custom model training, AutoML, MLOps tools, and seamless integration with Google Cloud services.

Prerequisites

• Basic knowledge of machine learning concepts
• Familiarity with Python programming
• Understanding of Google Cloud basics (optional)

Table of Contents

1. Introduction to Vertex AI
   1.1 Overview of Vertex AI and Its Use Cases
   1.2 Key Features of Vertex AI
   1.3 Comparison with Other Machine Learning Platforms
   1.4 Understanding Vertex AI’s Unified Approach
2. Setting Up Google Cloud for Vertex AI
   2.1 Creating a Google Cloud Project
   2.2 Enabling Vertex AI and Required APIs
   2.3 Setting Up Service Accounts and Permissions
   2.4 Navigating the Google Cloud Console
3. Data Preparation with Vertex AI
   3.1 Importing Data to Google Cloud Storage
   3.2 Data Labeling and Preprocessing with Vertex AI
   3.3 Managing Datasets and Versioning
   3.4 Best Practices for Data Quality and Bias Mitigation
4. Building and Training Models
   4.1 Introduction to Vertex AI Workbench
   4.2 Custom Model Training with Vertex AI Training Service
   4.3 Using AutoML for No-Code/Low-Code Model Training
   4.4 Distributed Training and Hyperparameter Tuning
5. Deploying Models with Vertex AI
   5.1 Understanding Model Deployment Options
   5.2 Deploying Models to Vertex AI Endpoints(Ref: Mastering Vertex AI: Building and Deploying ML Models at Scale )
   5.3 Implementing Model Versioning and Rollbacks
   5.4 Monitoring Deployed Models for Performance and Drift
6. Using Vertex AI Pipelines for Workflow Automation
   6.1 Introduction to ML Pipelines
   6.2 Building and Orchestrating Pipelines with Vertex AI
   6.3 Integrating Custom Components in Pipelines
   6.4 Managing Pipeline Versions and Lineage
7. Model Monitoring and Maintenance
   7.1 Monitoring Model Predictions and Performance
   7.2 Setting Up Alerts for Data Drift and Anomalies
   7.3 Retraining Models with Updated Data
   7.4 Using Explainable AI for Model Interpretability
8. Vertex AI and MLOps
   8.1 Overview of MLOps Principles
   8.2 Integrating CI/CD for ML Workflows
   8.3 Managing Model Lifecycle with Vertex AI Tools
   8.4 Collaboration and Governance in ML Projects
9. Integrating Vertex AI with Google Cloud Services
   9.1 Using BigQuery for Scalable Data Analysis
   9.2 Leveraging Dataflow for Data Pipelines
   9.3 Integrating Vertex AI with Cloud Functions and APIs
   9.4 Combining Vertex AI with Google Kubernetes Engine (GKE)
10. Hands-On Labs and Real-World Scenarios of Vertex AI Fundamentals
    10.1 Training an Image Classification Model with AutoML
    10.2 Building a Custom NLP Model with Vertex AI Training
    10.3 Deploying a Predictive Model and Setting Up Monitoring
    10.4 Automating an End-to-End ML Workflow with Vertex AI Pipelines

Conclusion of Vertex AI Fundamentals
Vertex AI empowers organizations to accelerate their machine learning initiatives by unifying the entire ML workflow on Google Cloud. This course has provided you with foundational knowledge and hands-on skills to leverage Vertex AI’s features, from data preparation to model deployment and monitoring. By integrating machine learning best practices with the power of Google Cloud, you’ll be well-equipped to tackle real-world ML challenges efficiently and effectively. Start exploring the limitless possibilities of Vertex AI to transform data into actionable insights.

Reference