MLOps
Prod ML
Concepts
MLOps Overview
Learn how MLOps extends DevOps to cover the entire ML lifecycle: data, training, deployment, and monitoring.
ML Lifecycle
- Data collection & versioning.
- Experiment tracking & model training.
- Model packaging & deployment.
- Monitoring, alerts, and retraining.
Key Components
CI/CD for ML
Automated testing and deployment of ML pipelines and models.
Monitoring
Track latency, errors, and data drift in production predictions.
Data & Model Versioning
Use tools like DVC, MLflow, or Weights & Biases to track artifacts.