What you'll learn:
- You will gain first-hand experience on how to train, optimize, deploy, and integrate ML in AWS cloud
- AWS Built-in algorithms, Bring Your Own, Ready-to-use AI capabilities
- Includes a high-quality Timed practice test (a lot of courses charge a separate fee for practice test)
- Zero Downtime Model Deployment
- How to Integrate and Invoke ML from your Application
- Automated Hyperparameter Tuning
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
