Description

Introduction

End-to-End Machine Learning Pipelines Using SageMaker is a comprehensive course designed for ML engineers, data scientists, and DevOps professionals who want to build, automate, and manage the entire machine learning lifecycle on AWS. By leveraging Amazon SageMaker Pipelines, you can streamline data preparation, model training, evaluation, deployment, and monitoring in a scalable and reproducible way. This course will walk you through building production-ready ML workflows that integrate seamlessly with other AWS services.

Prerequisites

To make the most of this course, learners should have:

• Intermediate knowledge of Python.

• Basic understanding of machine learning concepts and workflow stages.

• Familiarity with AWS services (especially SageMaker, S3, and IAM).

• Experience working with Jupyter notebooks.

Table of Contents

1. Introduction to ML Pipelines

   • 1.1 What Are ML Pipelines?

   • 1.2 Benefits of Automation and Reproducibility

   • 1.3 SageMaker Pipelines Overview

2. Setting Up Your SageMaker Environment

   • 2.1 Launching SageMaker Studio

   • 2.2 Creating and Managing Execution Roles

   • 2.3 Working with the SageMaker SDK

3. Defining the Pipeline Steps

   • 3.1 Data Ingestion from Amazon S3

   • 3.2 Data Processing and Feature Engineering

   • 3.3 Training and Hyperparameter Tuning

4. Model Evaluation and Registration

   • 4.1 Evaluating Model Metrics

   • 4.2 Conditional Logic in Pipelines

   • 4.3 Registering Models in SageMaker Model Registry

5. Model Deployment and Inference

   • 5.1 Deploying Models from Pipelines

   • 5.2 Configuring Real-Time Endpoints

   • 5.3 Batch Transform Integration

6. Monitoring and Managing Pipelines

   • 6.1 Tracking Pipeline Executions

   • 6.2 Logging with CloudWatch

   • 6.3 Integrating with SageMaker Model Monitor

7. CI/CD Integration and Automation

   • 7.1 Versioning and Reproducibility

   • 7.2 Integrating with AWS CodePipeline and CodeBuild

   • 7.3 Triggering Pipelines with Lambda or Events

8. Best Practices and Optimization

   • 8.1 Pipeline Design Patterns

   • 8.2 Managing Costs and Compute Resources

   • 8.3 Securing Pipelines with IAM and Encryption

By using SageMaker Pipelines, you can build reliable, automated, and scalable machine learning workflows that accelerate your model development and deployment. This course has equipped you with the knowledge to design, implement, and optimize end-to-end ML pipelines, making your solutions production-ready and easier to maintain. You’re now well-prepared to bring MLOps principles into your AWS-based machine learning projects.