Introduction
Vertex AI, a managed machine learning platform on Google Cloud, simplifies the process of deploying and serving machine learning models at scale. With powerful tools for model management, deployment, and monitoring, Vertex AI ensures that models are not only trained effectively but also deployed in production environments seamlessly. In this course, you will explore the entire deployment pipeline from model creation to serving, ensuring high availability, scalability, and real-time performance. Whether you’re deploying machine learning models for batch prediction or real-time inference, Vertex AI provides the infrastructure to meet your needs. This course will guide you through deploying models with minimal configuration, scaling them for performance, and monitoring their operation in production.

Prerequisites

• Basic understanding of machine learning and model deployment concepts
• Familiarity with Google Cloud Platform (GCP)
• Experience with Python and machine learning frameworks such as TensorFlow or PyTorch
• Basic understanding of cloud services like Google Cloud Storage, Google Compute Engine, and BigQuery

Table of Contents

1. Introduction to Vertex AI Model Deployment
   1.1 Overview of Vertex AI Deployment Features
   1.2 Key Concepts: Endpoints, Models, and Predictions
   1.3 Deployment Options: Batch vs. Online Predictions
   1.4 Benefits of Using Vertex AI for Model Deployment
2. Preparing Your Model for Deployment
   2.1 Exporting Trained Models in Supported Formats
   2.2 Converting Models for Vertex AI Deployment (TensorFlow, XGBoost, Scikit-Learn)
   2.3 Saving and Storing Models in Google Cloud Storage
   2.4 Verifying Model Integrity and Compatibility
3. Deploying Models to Vertex AI
   3.1 Creating a Model Resource in Vertex AI(Ref: Using Vertex AI for Custom Model Training and Hyperparameter Tuning)
   3.2 Deploying Models for Batch Prediction
   3.3 Deploying Models for Online (Real-Time) Prediction
   3.4 Configuring Model Resources for Auto-Scaling
   3.5 Managing Model Versions and Updates
4. Real-Time Prediction with Vertex AI
   4.1 Setting Up Endpoints for Real-Time Inference
   4.2 Sending Real-Time Prediction Requests via API
   4.3 Optimizing Prediction Speed and Latency
   4.4 Implementing Custom Prediction Requests and Responses
   4.5 Monitoring and Scaling Real-Time Prediction Services
5. Batch Prediction with Vertex AI
   5.1 Overview of Batch Prediction in Vertex AI
   5.2 Creating Batch Prediction Jobs and Configurations
   5.3 Managing Input and Output Data for Batch Prediction
   5.4 Scheduling and Automating Batch Prediction Jobs
   5.5 Optimizing Batch Prediction for Large Datasets
6. Managing Model Deployment at Scale
   6.1 Auto-Scaling and Load Balancing for Model Endpoints
   6.2 Handling Traffic Spikes and Predictive Scaling
   6.3 Distributing Models Across Regions for High Availability
   6.4 Monitoring Model Health and Resource Utilization
   6.5 Managing Multiple Models in a Single Endpoint
7. Security and Access Control for Model Deployment
   7.1 Configuring IAM Roles and Permissions for Vertex AI
   7.2 Securing Model Endpoints with HTTPS and Authentication
   7.3 Setting Up Identity-Aware Proxy (IAP) for Secure API Access
   7.4 Monitoring and Logging with Google Cloud’s Operations Suite
   7.5 Best Practices for Securing Model Deployment in Production
8. Model Monitoring and Management
   8.1 Monitoring Model Performance in Production
   8.2 Setting Up Alerts and Notifications for Model Issues
   8.3 Logging and Debugging Model Inference Requests
   8.4 Retraining Models Based on Performance Feedback
   8.5 Implementing A/B Testing and Model Version Management
9. Integrating Vertex AI with Other Google Cloud Services
   9.1 Using Google Cloud Functions for Event-Driven Prediction
   9.2 Integrating with BigQuery for Data-Driven Prediction
   9.3 Automating Predictions with Cloud Run and Vertex AI(Ref: Using Vertex AI for Custom Model Training and Hyperparameter Tuning)
   9.4 Using Pub/Sub for Real-Time Data Ingestion and Prediction
   9.5 Integrating with Dataflow for Advanced ML Pipelines
10. Best Practices for Model Deployment and Serving
    10.1 Ensuring Model Performance and Scalability in Production
    10.2 Optimizing Model Serving with TensorFlow Lite, TensorFlow Serving, and TFX
    10.3 Managing Model Drift and Continuous Monitoring
    10.4 Cost Management Strategies for Model Serving in Vertex AI
    10.5 Future-Proofing Your ML Models in Production
11. Hands-On Projects and Real-World Scenarios
    11.1 Deploying a Text Classification Model for Real-Time Inference
    11.2 Batch Prediction for Customer Segmentation with Vertex AI
    11.3 Building an End-to-End ML Pipeline for Predictive Analytics
    11.4 Monitoring and Scaling a Model in a Multi-Region Setup

Conclusion
Deploying and serving machine learning models with Vertex AI provides a streamlined, scalable solution for bringing your models into production. This course has equipped you with the knowledge to deploy custom models for both real-time and batch predictions, manage model resources effectively, and integrate Vertex AI with other Google Cloud services. With Vertex AI’s robust security, scalability, and monitoring tools, you are prepared to deploy models that meet the demands of real-world applications. By applying best practices and leveraging the capabilities of Vertex AI, you can ensure that your models run efficiently and securely at scale. Whether for enterprise use or experimental projects, Vertex AI will support the continuous growth and optimization of your machine learning models in production.