Deploy ML models in AWS Sagemakers
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Deploy ML models in AWS Sagemaker

  • Learn to Build, Train and Deploy ML Models with AWS Sagemaker in depth.
    Sagemaker allows you to do production grade deployment and everything you need in ML lifecycle.

  • Covers sagemaker specific details, industry best practices and fully worked out code with live demonstration.

Created by Selva Prabhakaran

  • 31 Video Lessons

  • English

  • English captions

What you will learn


What is AWS Sagemaker


How Sagemaker works internally


How to build ML models with Sagemaker


How to do preprocessing in separate container


Deploy via Batch processing and Model Endpoints


Perform and track ML Experiments

Course Curriculum


  • Courses Page1 Basics of Python
  • Courses Page1 Foundational knowledge of Data Science
  • Courses Page1 High school maths

Who should attend this course?

  • Data Science Aspirants

  • Data Science Professionals

  • Software/Data engineers interested in quantitative analysis

  • Professionals working with large datasets

  • Data analysts, economists, researchers


Selva Prabhakaran Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this project!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I’ve deployed 20+ global products. I’m also the Founder & Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I’ve put up the series of courses and projects with detailed explanations – just like an on the job experience. Hope you love it!

  • 4.5+Instructor rating

  • 200+ reviews

  • 10K+students

  • 15+ Courses


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