AWS Machine Learning with SageMaker: Hands-On

About this course

Build, train, and deploy real machine learning models on AWS using SageMaker—through hands-on labs and real-world projects.

This course is designed for developers, data engineers, and aspiring ML practitioners who want practical experience building end-to-end machine learning solutions in the cloud.

You won’t just learn theory—you’ll actually build and deploy models.

What you’ll learn

Set up and use AWS SageMaker for ML workflows

Prepare data: handle missing values, mixed data types, and feature engineering

Train, tune, and evaluate machine learning models

Deploy models into production and integrate with applications

Use Hugging Face and DeepSeek LLMs on AWSPerform A/B testing and safely update production models

Build recommender systems, time-series models, and anomaly detection solutions

Apply model explainability and fairness techniques

Secure your ML workloads on AWSHands-On Learning Experience

Through guided labs, you will:

• Train and deploy your first SageMaker model
• Work with built-in algorithms and custom containers (PyTorch, TensorFlow)Optimize models using automated hyperparameter tuning
• Build real-world ML pipelines from scratch
• Modern AI & LLMsGo beyond traditional ML:Deploy Hugging Face models on SageMaker
• Work with DeepSeek LLMs
• Understand how modern AI fits into AWS workflows

Production-Ready ML

Learn how to:

• Continuously improve models
• Run A/B tests
• Roll back safely with zero downtime
• Who this course is for
• Developers new to machine

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