AWS Machine Learning with SageMaker: Hands-On
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
Requirements
- Familiarity with Python
- AWS Account - I will walk through steps to setup one
- Basic knowledge of Pandas, Numpy, Matplotlib
- Be an active learner and use course discussion forum if you need help - Please don't put help needed items in course review
Description
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
Who this course is for:
- This course is designed for anyone who is interested in AWS cloud based machine learning and data science
Instructor
Chandra Lingam is an experienced professional in AWS, with a strong background in mission-critical systems and machine learning. He has a wealth of knowledge and expertise in systems development for both traditional IT data centers and cloud computing.
Chandra's courses on the AWS certification have been highly praised and were even mentioned in a recent Udemy earnings call by the CEO. Prior to his work as a course developer and instructor, he spent 15 years as a software engineer at Intel.
Chandra holds a Master's in Computer Science from Arizona State University and a Bachelor's in Computer Science from Thiagarajar College of Engineering in Madurai.
His courses offer valuable and up-to-date content for both beginners and experienced IT professionals looking to advance their skills in AWS and related technologies.
