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
Curriculum
- 12 Sections
- 67 Lessons
- 10 Weeks
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- Introduction4
- MLOps Fundamentals3
- AWS and MLOps7
- AWS Specific Tools and Configurations11
- 4.1DevOps Lifecycle Tools in AWS
- 4.2Creating and Configuring an AWS Account
- 4.3Security Setup: MFA, IAM Accounts, and Policies
- 4.4Introduction to S3 Buckets and EC2 Instances
- 4.5AWS Specific Tools and Configurations
- 4.6Creation of S3 Bucket from Console
- 4.726. Creation of S3 Bucket from CLI
- 4.8Version Enablement in S3
- 4.9Introduction EC2 instances
- 4.10Launch EC2 instance & SSH into EC2 Instances
- 4.11Housekeeping Activity
- Linux and Bash for MLOps3
- Core Concepts10
- 6.1CI/CD Pipeline Introduction
- 6.2Getting Started with AWS CodeCommit & Distributed Version Control Systems (DVCS)
- 6.3Initial Configuration & Basic Git Commands
- 6.4Setting Up Your Git Workspace
- 6.5Understanding Git Workflow
- 6.6Adding Files to the Staging Area & Understanding Staged Differences
- 6.7Unstaging, Resetting, and Reverting Changes in Git
- 6.8Working with AWS CodeCommit: Remote Commands, Security, and Integrations
- 6.9Cloning, Branching, and Handling Git Branches: Hands-On Parts 1 & 2
- 6.10Resolving Git Conflicts, Rebasing vs. Merging, and Using Git Stash
- Deployment & Security8
- 7.1Introduction to AWS CodeDeploy & YAML
- 7.2First Steps with AWS CodeDeploy: Hands-On Introduction and Deep Dive
- 7.3Exploring AWS CodePipeline: Creation and Automation with Manual Approval
- 7.4Introduction to Docker: Basics & Installation
- 7.5Pull the image from Docker Desktop
- 7.6Dockerfile
- 7.7Push the Docker Image to ECR
- 7.8Hands on – Amazon ECR for AWS CodeBuild
- Amazon SageMaker & Feature Engineering11
- 8.1Why Amazon SageMaker is Preferred for Machine Learning Workflows
- 8.2Domain Creation, Studio Setup, and Clean-Up Activities in SageMaker
- 8.3Feature Engineering Essentials, Data Wrangler Setup, and Transformation Techniques
- 8.4Data Quality and Insights Report
- 8.5Univariate Analysis & Bias Report
- 8.6Target Leakage
- 8.7Data Transformation
- 8.8Data Transformation – Custom Script
- 8.9Export to S3
- 8.10Feature Engineering on Sagemaker Notebook Instance
- 8.11Summary
- Advanced Concepts3
- Building and Managing MLOps Pipelines3
- Packaging, Deployment, and Kubernetes3
- DevOps1
