Course Overview
This comprehensive course is designed to equip you with the skills needed to become proficient in MLOps & DevOps practices using AWS services. You’ll learn how to automate and streamline your machine learning and development operations to improve efficiency, repeatability, and reliability.
Who This Course is For
Data Scientists interested in deploying machine learning models at scale
DevOps Engineers looking to specialize in machine learning projects
IT Professionals seeking to understand AWS services for MLOps & DevOps
Prerequisites
Basic knowledge of AWS services
Understanding of DevOps principles
Familiarity with machine learning concepts
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Introduction
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MLOps Fundamentals
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AWS and MLOps
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AWS Specific Tools and Configurations
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DevOps Lifecycle Tools in AWS
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Creating and Configuring an AWS Account
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Security Setup: MFA, IAM Accounts, and Policies
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Introduction to S3 Buckets and EC2 Instances
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AWS Specific Tools and Configurations
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Creation of S3 Bucket from Console
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26. Creation of S3 Bucket from CLI
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Version Enablement in S3
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Introduction EC2 instances
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Launch EC2 instance & SSH into EC2 Instances
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Housekeeping Activity
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Linux and Bash for MLOps
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Core Concepts
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CI/CD Pipeline Introduction
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Getting Started with AWS CodeCommit & Distributed Version Control Systems (DVCS)
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Initial Configuration & Basic Git Commands
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Setting Up Your Git Workspace
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Understanding Git Workflow
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Adding Files to the Staging Area & Understanding Staged Differences
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Unstaging, Resetting, and Reverting Changes in Git
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Working with AWS CodeCommit: Remote Commands, Security, and Integrations
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Cloning, Branching, and Handling Git Branches: Hands-On Parts 1 & 2
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Resolving Git Conflicts, Rebasing vs. Merging, and Using Git Stash
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Deployment & Security
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Introduction to AWS CodeDeploy & YAML
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First Steps with AWS CodeDeploy: Hands-On Introduction and Deep Dive
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Exploring AWS CodePipeline: Creation and Automation with Manual Approval
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Introduction to Docker: Basics & Installation
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Pull the image from Docker Desktop
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Dockerfile
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Push the Docker Image to ECR
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Hands on - Amazon ECR for AWS CodeBuild
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Amazon SageMaker & Feature Engineering
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Why Amazon SageMaker is Preferred for Machine Learning Workflows
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Domain Creation, Studio Setup, and Clean-Up Activities in SageMaker
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Feature Engineering Essentials, Data Wrangler Setup, and Transformation Techniques
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Data Quality and Insights Report
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Univariate Analysis & Bias Report
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Target Leakage
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Data Transformation
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Data Transformation - Custom Script
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Export to S3
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Feature Engineering on Sagemaker Notebook Instance
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Summary
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Advanced Concepts
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Building and Managing MLOps Pipelines
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Packaging, Deployment, and Kubernetes
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DevOps
