Practical MLOps: AWS Mastery for Data Scientists & DevOps

Description

In this course, you will learn:

• Configuring the CI/CD Pipeline for Machine Learning Projects
• AWS CodeCommit allows you to track source code, training pictures, and configuration files using a Git-based repository.
• Ability to build using AWS CodeBuild.
• The ability to deploy the application to a server using AWS CodeDeploy.
• Create an AWS CodePipeline to orchestrate the MLOps steps.
• Determine which AWS services are most appropriate for implementing ML solutions.
• Perform the Load testing
• Monitoring the End Point Performance
• Monitoring the Model Drift
• The ability to follow model-training best practices
• The ability to follow deployment best practices
• The ability to follow operational best practices

Syllabus:

• About AWS MLOps Course and Instructor
• Introduction to MLOps
• DevOps for Data Scientists
• Getting Started with AWS
• Linux Operating System for DevOps and Data Scientists
• Source code Management using GIT - CodeCommit
• YAML Crash Course
• AWS CodeBuild
• AWS Code Deploy
• Code Pipeline
• Docker Containers
• Practical MLOps - Amazon Sagemaker
• Feature Engineering - Feature Store in Sagemaker
• Training, Tuning & Deploying the Model
• Create Custom Models
• AWS CloudFormation
• AWS Step Functions
• MLOps Sagemaker Pipelines
• Expert Guidance on MLOps