ML Engineering
MLOps - From Notebook to Production, Done Right
Master the practices and tools for deploying, monitoring, and maintaining ML models in production.
- CI/CD for ML - automate model training and deployment
- Model monitoring - track performance and drift in production
- Reproducibility - ensure consistent results across environments
Automation is not about replacing humans; it's about augmenting them.
MLOps — Machine Learning Operations
MLOps applies DevOps principles to ML — automating model training, deployment, monitoring, and maintenance.
MLOps Lifecycle
MLOps Lifecycle Diagram
Experiment Tracking
CI/CD Pipeline for ML
Model Monitoring
Model Monitoring Dashboard
Key Takeaways
What to Learn Next
-> Model Deployment Deploy models to production environments.
-> Feature Stores Manage and serve features efficiently.
-> ML System Design Design scalable ML architectures.
-> Model Evaluation Measure model performance accurately.
-> Model Selection Choose the right model for your problem.
-> AutoML Automate machine learning workflows.