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.
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.
Special Introductory Price $1800
Self Pay or Company Sponsored
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.
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.
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.