About the workshop

Curriculum

Day 1

Session-1: 10:00 AM to 2:30 PM (IST)

AWS SageMaker

• Introduction to Amazon Sagemaker
• Using Amazon S3 along with Sagemaker [Hands-on]
• Amazon SageMaker Notebooks and SDK [Hands-on]
• Notebook instance type, IAM Role & VPC [Hands-on]
• Build, Train & deploy ML Model using TensorFlow [Hands-on]
• Endpoint & Endpoint configurations [Hands-on]
• Generate inference from deployed model [Hands-on]

Day 2

Session-2: 10:00 AM to 2:30 PM (IST)

AWS Machine Learning Operations (MLOps)

• Introduction Sagemaker pipelines
• AWS CodeCommit [Hands-on]
• AWS CodePipeline [Hands-on]
• AWS CodeBuild [Hands-on]
• Containers for Amazon SageMaker
• AWS MLOps - ML Model Monitoring
• AWS MLOps - Amazon Sagemaker Feature Store
• AWS MLOps - Post-Deployment Challenges

AWS MLOps - Build, Train & deploy ML Model

• SageMaker Studio & SageMaker domain [Hands-on]
• SageMaker Projects [Hands-on]
• Repositories [Hands-on]
• Pipelines & Graphs [Hands-on]
• Experiments [Hands-on]
• Model groups [Hands-on]
• Endpoints [Hands-on]

Do you know?

This is the best time to learn machine learning as the trends in the market suggest. The global machine learning market is estimated at US dollar 8.43 billion in 2019 and is expected to reach 117 billion by 2027, at a CAGR of 39.2%. Thus, job opportunities in this sector are going to grow with a boom in the coming years.

AI and machine learning are not only used in machine learning applications but also in the Internet of things, like self-driving cars, smart homes, digital assistants, etc. In fact, during COVID-19, statistical machine learning had played a significant role in generating advanced models for predicting virus spread, and aided in the management of the pandemic across the world. Machine learning in finance has also secured a respectable place among the business leaders using the technology for generating automatic models for stock management.

Syllabus

What you’ll learn

Reviews and Testimonials