Practical AI with AWS Sagemaker

Learn how to build, train, and deploy machine learning models at scale using Amazon SageMaker through this practical hands-on course

Course description

This course will provide you with the skills required to build and deploy machine learning models on Amazon cloud. You will gain exposure to Amazon SageMaker capabilities such as Studio, Autopilot, Data Wrangler, Pipelines, and Feature Store.

Class dates and schedule

Next start dates TBA

Monday - Friday, 12:00-1:00 PM (lunch hour)

A full month of live, online experiential learning on how to implement AI and ML models for commercial use.

Class fee

Special Introductory Price $1800
Self Pay or Company Sponsored

Email for more info

Targeted outcomes for this course

  • Build, train, and deploy machine learning models quickly using Amazon SageMaker
  • Optimize the accuracy, cost, and fairness of your models
  • Create and automate end-to-end machine learning workflows on Amazon Web Services (AWS)

Who this course if for

This course is for anybody who wants to learn how to use Amazon SageMaker to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics, Python programming language, and machine learning concepts is required. Registered participants will be provided links to study material that will prepare them for the course. Early signup is encouraged as it will provide you with time to prepare.


  • 1-2 hours of your time every day for 4 weeks.
  • Commitment to learn something new.
  • Finishing pre-requisite course material in Python and Machine learning basics
  • An AWS account to run lab exercises. (AWS provides a free-tier that allows you to try out several of AWS services within certain limits. A credit card might be required to use high-end GPU virtual machines. We will use small datasets in this course that do not require high end processing power)
  • You will need a laptop or desktop for this course.
  • The course will be supplemented with a required text book. This book will be provided to the student in electronic form at no charge to the student. You can buy a printed copy if necessary.

Full Course Description

Machine learning (ML) infrastructure is the foundation on which machine learning models are developed and deployed. Having a solid infrastructure to quickly build, train, and deploy machine learning models at scale is a key requirement for practitioners. Amazon SageMaker is the industry leading platform that allows developers and data scientists focus on the machine learning problem without worrying about the infrastructure. In this course, we will cover how developers can utilize SageMaker features such as Data Wrangler, Pipelines, Clarify, Feature Store, and much more.

We will start with learning about the capabilities of SageMaker as a single platform to solve ML challenges and move on to learn features such as AutoML, built-in algorithms and frameworks, and even writing your own code and algorithms to build ML models into SageMaker.

We will progress to learn how to integrate Amazon SageMaker with popular deep learning libraries, such as TensorFlow and PyTorch. Finally, we will learn how to get your models to production faster with minimum effort and at a lower cost using workflow automation. We’ll also use SageMaker Debugger and SageMaker Model Monitor to detect quality issues in training and production.

By the end of this course, you'll be able to use Amazon SageMaker to create end-to-end workflow, from experimentation, training, and monitoring to scaling, deployment, and automation.

What you will learn

  • Data annotation and data preparation techniques
  • Use AutoPilot to build and train machine learning models with zero coding
  • Create models using built-in algorithms and frameworks and your own code
  • Explore real-world problems and solve them using computer vision and natural language processing (NLP) models
  • Advanced techniques for scaling, model optimization, model debugging, and cost optimization
  • Automate model deployment tasks using SDK and automation tools

Meet the instructor

Raj Anantharman is currently the chief executive officer at Crannium Health AI. Apart from publishing several papers in healthcare AI, Raj spent the last 5 years at Crannium developing Healthcare AI models for commercial use. Prior to Crannium, Raj served for 10 years as Co-Founder and CTO for Yoodle, a digital agency servicing higher education and government clients where he led large-scale Web development and Cloud projects.