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MLOps Fundamentals

Learn how to take your ML models from Jupyter notebooks to production systems that actually work in the real world.

Why MLOps Matters

I've seen too many great ML models die in notebooks. MLOps is about building systems that are: - Reliable - They work when you need them - Scalable - They handle real-world data volumes - Maintainable - You can update them without breaking everything - Monitored - You know when something goes wrong

Course Structure

Part 1: Version Control for ML

  • Git for data scientists
  • DVC for data versioning
  • Model versioning strategies
  • Experiment tracking with MLflow

Part 2: Building ML Pipelines

  • Pipeline design patterns
  • Data validation and testing
  • Feature stores
  • Model training automation

Part 3: Model Deployment

  • Containerization with Docker
  • API development with FastAPI
  • Cloud deployment strategies
  • A/B testing for models

Part 4: Monitoring & Maintenance

  • Model performance monitoring
  • Data drift detection
  • Automated retraining
  • Incident response

Real-World Project

Throughout the course, we'll build a complete MLOps pipeline for a recommendation system, from data ingestion to production deployment.

Tools & Technologies

  • Version Control: Git, DVC
  • Experiment Tracking: MLflow, Weights & Biases
  • Orchestration: Apache Airflow, Prefect
  • Deployment: Docker, Kubernetes, FastAPI
  • Monitoring: Prometheus, Grafana, custom dashboards

This course is based on patterns I've implemented across multiple production ML systems. Real experience, real problems, real solutions.