MLOps Mastery

Master the art of Machine Learning Operations (MLOps) by learning to manage, deploy, and monitor ML models at scale using industry-standard tools.

Course Plan

Week 1: Introduction to MLOps

Overview of MLOps principles and lifecycle
Differences between DevOps and MLOps
Key tools and frameworks in MLOps

Week 2: Data Management and Versioning

Data collection and preprocessing
Versioning datasets with DVC
Storing data in AWS S3

Week 3-4: Model Development

Building models with TensorFlow and PyTorch
Experiment tracking with MLflow
Hyperparameter tuning

Week 5-6: Model Training at Scale

Training models with AWS SageMaker
Distributed training with Horovod
Managing compute resources with AWS EC2

Week 7-8: Model Deployment

Containerizing models with Docker
Deploying models with Kubernetes
Real-time inference with AWS SageMaker Endpoints

Week 9: Monitoring and Logging

Monitoring model performance with AWS CloudWatch
Logging predictions and metrics with MLflow
Setting up alerts for model drift

Week 10: CI/CD for ML Pipelines

Automating ML workflows with GitHub Actions
Building CI/CD pipelines with Jenkins
Continuous training and deployment

Week 11: Security and Governance

Securing models with AWS IAM
Data privacy and compliance
Model governance best practices

Week 12: Capstone Project

Build an end-to-end MLOps pipeline
Deploy a scalable ML model on AWS
Monitor and optimize the deployed model

We Will Cover Tools

AWS S3

AWS EC2

AWS SageMaker

AWS CloudWatch

AWS IAM

Docker

Kubernetes

Git

GitHub Actions

Jenkins

DVC

TensorFlow

PyTorch

MLflow

Horovod