What is MLOps?
MLOps (Machine Learning Operations) is the practice of deploying, monitoring, and maintaining machine learning models in production. It combines ML, DevOps, and Data Engineering to automate the entire ML lifecycle.
As companies move from experimental ML to production AI, MLOps engineers are in high demand to bridge the gap between data science and production systems.
What You'll Learn in MLOps Training
- ✓ ML Pipeline Automation - End-to-end automated training pipelines
- ✓ Model Deployment - REST APIs, batch inference, edge deployment
- ✓ MLflow - Experiment tracking, model registry, deployment
- ✓ Kubeflow - Kubernetes-native ML pipelines
- ✓ AWS SageMaker - End-to-end ML platform
- ✓ Model Monitoring - Drift detection, performance tracking
- ✓ Feature Stores - Feast, Tecton, feature engineering at scale
MLOps Training Curriculum
- Module 1: MLOps Fundamentals & ML Lifecycle
- Module 2: Data Pipeline Engineering
- Module 3: Experiment Tracking with MLflow
- Module 4: Model Packaging & Containerization
- Module 5: Model Deployment Strategies
- Module 6: Kubeflow & Kubernetes for ML
- Module 7: AWS SageMaker & Vertex AI
- Module 8: Model Monitoring & Drift Detection
- Module 9: Feature Stores & ML Infrastructure
- Module 10: Enterprise MLOps Project
