Description

Introduction
Vertex AI Pipelines is a powerful tool within Google Cloud’s Vertex AI platform that enables the automation, orchestration, and management of machine learning (ML) workflows. This course is designed to teach you how to build, deploy, and manage end-to-end ML pipelines using Vertex AI Pipelines. By the end of this course, you will be proficient in creating repeatable and scalable workflows for machine learning projects, ensuring consistency, efficiency, and collaboration across teams. Learn how to integrate data preparation, model training, evaluation, and deployment into a seamless, automated pipeline for machine learning applications.

Prerequisites

• Basic understanding of machine learning concepts
• Familiarity with Python programming and ML frameworks
• Experience with Google Cloud Platform (preferably Vertex AI)
• Knowledge of Kubernetes and Docker (optional but beneficial)

Table of Contents

1. Introduction
   1.1 Overview
   1.2 Benefits of Automating ML Workflows with Pipelines
   1.3 Key Components
   1.4 Real-World Applications of ML Pipelines
2. Setting Up
   2.1 Creating and Configuring this in Google Cloud
   2.2 Understanding Pipeline Components and Tasks
   2.3 Setting Up Kubernetes Cluster
   2.4 Integrating with Google Cloud Storage, BigQuery, and other GCP services
3. Building Machine Learning Pipelines
   3.1 Designing and Defining ML Pipelines Using Python SDK
   3.2 Creating Pipeline Components for Data Processing and Preprocessing
   3.3 Defining Custom Components for Model Training and Evaluation
   3.4 Integrating Third-Party Libraries and Frameworks in Pipelines
4. Pipeline Orchestration and Execution
   4.1 Running Pipelines on Google Cloud
   4.2 Triggering Pipelines with Scheduled Events
   4.3 Managing Pipeline Execution and Monitoring Progress
   4.4 Using Pipeline Execution Logs for Debugging
5. Automating Model Training and Hyperparameter Tuning
   5.1 Automating Data Splitting and Feature Engineering in Pipelines
   5.2 Running Distributed Training Jobs within Pipelines
   5.3 Implementing Hyperparameter Tuning for Optimal Model Performance
   5.4 Automating Retraining and Model Updates in Production
6. Model Deployment and Serving with Pipelines
   6.1 Deploying Trained Models to Vertex AI for Real-Time Inference
   6.2 Managing Batch Predictions Using Pipelines
   6.3 Automating Model Versioning and Updates
   6.4 Scaling Model Deployment with Kubernetes and Vertex AI
7. Integrating Vertex AI Pipelines with MLOps
   7.1 Understanding MLOps in the Context
   7.2 Versioning Data, Models, and Pipelines for Reproducibility
   7.3 Integrating with CI/CD Systems for Continuous Delivery of ML Models
   7.4 Collaborating with Data Engineers and DevOps Teams
8. Monitoring and Managing Pipelines
   8.1 Monitoring Pipeline Performance and Results
   8.2 Setting Up Alerts for Pipeline Failures and Anomalies
   8.3 Using Vertex AI for Model Drift Detection and Retraining
   8.4 Ensuring Scalability and Reliability in Production Pipelines
9. Security and Governance
   9.1 Securing Data and Models in Vertex AI Pipelines(Ref: Vertex AI for Data Scientists: End-to-End Machine Learning Workflow)
   9.2 Implementing Role-Based Access Control (RBAC) for Team Collaboration
   9.3 Auditing and Compliance in Machine Learning Pipelines
   9.4 Best Practices for Securing Model Deployment and Data Integrity
10. Advanced Techniques
    10.1 Implementing Advanced Pipelines for Multi-Step ML Workflows
    10.2 Leveraging Custom Containers and ML Frameworks in Pipelines
    10.3 Automating Data Labeling and Preprocessing Tasks
    10.4 Integrating External APIs and Services for End-to-End Pipelines
11. Hands-On Projects and Real-World Use Cases
    11.1 Building an End-to-End ML Pipeline for Image Classification
    11.2 Automating Customer Churn Prediction
    11.3 Implementing a Time Series Forecasting Pipeline
    11.4 Deploying a Recommender System

Conclusion
Mastering Vertex AI Pipelines allows data scientists, machine learning engineers, and teams to automate and manage the full lifecycle of ML workflows with scalability and efficiency. This course equips you with the tools to streamline model training, deployment, and monitoring while ensuring reproducibility and security in the machine learning pipeline. By leveraging the power of Vertex AI Pipelines, you can take your ML projects from development to production with minimal manual intervention, enabling continuous improvement, faster iteration cycles, and more reliable results. Completing this course will help you build robust, production-ready ML systems that can scale to meet the demands of modern AI applications.

Reference