AWS Machine Learning with SageMaker: Hands-On

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 AWS

• Perform 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 AWS

Hands-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 & LLMs

Go 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 learning on AWS

• Engineers who want hands-on SageMaker experience

• Anyone looking to build and deploy ML models in production