AWS-Powered End-to-End MLOps and GitOps

Welcome! We’re thrilled to have you join us in this hands-on journey to master MLOps and GitOps with the power of AWS! Whether you’re a data scientist, developer, or DevOps engineer, this course is designed to help you understand and implement scalable and efficient processes for managing machine learning workflows and infrastructure using modern best practices.

What You’ll Learn

This course will equip you with the skills to:

Build Scalable ML Pipelines:
- Automate the ML lifecycle using AWS services like SageMaker, Lambda, and Step Functions.
- Deploy models to production with minimal manual intervention.
Implement GitOps for Infrastructure as Code (IaC):
- Leverage Git as the source of truth for managing infrastructure and application updates.
- Automate deployments using tools like AWS CodePipeline, CodeBuild, and CodeDeploy.
Unite MLOps and GitOps:
- Bridge the gap between model development and reliable deployment pipelines.
- Apply GitOps principles to ML model versioning and deployment.
Master AWS Ecosystem:
- Work with Amazon SageMaker for training, testing, and deploying ML models.
- Manage containers with Amazon ECS or EKS.
- Use Terraform for infrastructure automation.
Collaborate and Scale:
- Implement CI/CD workflows for ML pipelines.
- Utilize monitoring tools like Amazon CloudWatch, OpenSearch, and Prometheus to track model and system performance.

Who is This Course For?

This course is tailored for:

Data Scientists looking to streamline their model development and deployment.
DevOps Engineers interested in scaling ML workflows and introducing GitOps to MLOps.
Cloud Engineers seeking hands-on AWS experience with practical projects.
Developers aiming to understand end-to-end ML deployment processes.

Key AWS Services Covered

Amazon SageMaker: ML model training, tuning, and deployment.
Amazon ECR and ECS/EKS: Container registry and orchestration for scalable deployments.
AWS CodePipeline: CI/CD for automated deployments.
Amazon CloudWatch: Monitoring and logging for infrastructure and applications.
AWS Secrets Manager: Secure key and credentials management.

Course Structure

Introduction to MLOps and GitOps Principles
Setting Up AWS for MLOps: Prerequisites and Best Practices
Building an ML Pipeline with SageMaker
Containerizing and Deploying Models
Integrating GitOps with MLOps
CI/CD Pipelines for ML Applications
Monitoring and Continuous Feedback Loops
Capstone Project: Deploying a Real-World ML Application

Learning Approach

Hands-On Labs: Follow along with guided exercises using AWS resources.
Real-World Scenarios: Tackle practical challenges with a focus on production-ready solutions.
Capstone Project: Build and deploy an ML-powered app using MLOps and GitOps workflows.