MLOps
MLOps introduces the fundamental concepts of deploying, managing, and maintaining Machine Learning models in production. This course provides a beginner-friendly overview of the Machine Learning lifecycle, model deployment, automation, and monitoring using popular MLOps tools and cloud platforms. Students will learn how to organize ML projects, track experiments, deploy trained models, and understand the basic workflows required to build reliable and scalable AI applications.
01
Introduction to MLOps
- Introduction to MLOps
- Machine Learning Lifecycle
- MLOps vs DevOps
- Why MLOps is Important
- Production ML Workflow
02
Model & Experiment Management
- Introduction to Experiment Tracking
- Model Versioning
- Dataset Versioning
- Basic MLflow
- Model Registry Concepts
03
Model Deployment Basics
- Batch vs Real-Time Inference
- Deploying ML Models
- REST APIs for ML Models
- Introduction to Docker for ML
- Basic Deployment Workflow
04
Monitoring & Automation
- Model Monitoring Basics
- Data Drift & Model Drift
- Model Retraining Concepts
- Introduction to CI/CD for ML
- MLOps Best Practices
