MLOps

1Index 2MLOps Kya Hai 3ML Lifecycle 4DevOps vs MLOps 5MLOps Maturity Levels 6MLOps Roles 7MLOps Tools Landscape 8Git for ML 9DVC Data Versioning 10MLflow Intro 11MLflow Advanced 12Weights and Biases 13Model Registry 14Reproducibility 15Data Pipelines 16Feature Stores 17Data Quality 18Data Labeling 19Synthetic Data 20Data Storage 21Training Pipelines 22Hyperparameter Tuning 23Distributed Training 24AutoML 25Model Optimization 26GPU Management 27Docker Fundamentals 28Docker for ML 29Docker Compose 30Container Registries 31Model Packaging 32CICD Concepts 33GitHub Actions 34Orchestration 35Airflow 36Testing ML 37Continuous Training 38Deployment Strategies 39REST API 40Serving Frameworks 41Kubernetes Basics 42Kubernetes Advanced 43Serverless ML 44Edge Deployment 45Batch vs Realtime 46Monitoring Basics 47Data Drift 48Model Drift 49Monitoring Tools 50Logging Alerting 51AB Testing 52Explainability 53SageMaker 54Vertex AI 55Azure ML 56Cost Optimization 57Multi Cloud 58IaC 59LLMOps Intro 60LLM Serving 61LLM Eval 62LLM Monitoring 63RAG Ops 64Fine Tuning Ops 65ML Security 66ML Governance 67Platform Design 68Case Studies 69E2E Project 70Best Practices 71Career Guide