MLOps Tools
MLOps (Machine Learning Operations) brings DevOps practices to ML. It covers the entire ML lifecycle: data versioning, experiment tracking, model deployment, and monitoring.
Goal: Automate and streamline ML workflows from development to production.
MLOps Lifecycle
1
Data Management
Version datasets, track lineage
2
Experiment Tracking
Log metrics, hyperparameters, artifacts
3
Model Training
Distributed training, hyperparameter tuning
4
Model Registry
Store and version models
5
Deployment
Serve models via APIs
6
Monitoring
Track performance, detect drift
Popular Tools
MLflow
Experiment tracking, model registry, deployment
→ Open-source, framework-agnostic
Weights & Biases
Experiment tracking, visualization, collaboration
→ Best-in-class UI, team features
DVC
Data version control, pipeline management
→ Git-like versioning for data
Kubeflow
ML workflows on Kubernetes
→ Enterprise-scale deployment
Docker
Containerization for reproducibility
→ Package models with dependencies
Key Takeaway: MLOps is essential for production ML. Use tools like MLflow for tracking, Docker for packaging, and monitoring for detecting model drift.