MLOps та DevOps для Machine Learning

🎓 Components of MLOps

Version Control

Code: Git for code, scripts, notebooks.

Data: DVC (Data Version Control) for data, datasets.

Models: MLflow, Weights & Biases for models, experiments.

Importance: Reproducibility, change tracking.

CI/CD Pipeline

CI: Continuous Integration. Automatic testing upon changes.

CD: Continuous Deployment. Automatic deployment after tests pass.

ML-specific: Data validation, model tests, performance checks.

Monitoring

Performance: Accuracy, latency, throughput.

Data Drift: Changes in the distribution of input data.

Model Drift: Decrease in model performance.

Infrastructure: CPU, memory, GPU usage.

🔧 Tools and Platforms

MLflow

Components: Tracking (experiments), Projects (packaging), Models (deployment), Registry (model registry).

Advantages: Open-source, integration with many tools.

Applications: Experiment tracking, model versioning, deployment.

Kubeflow

Concept: ML workflows on Kubernetes.

Components: Pipelines, Training, Serving, Notebooks.

Advantages: Scalable, cloud-native.

Weights & Biases

Concept: Experiment tracking, visualization, collaboration.

Advantages: Good for teams, visualization.

Applications: Experiment tracking, hyperparameter optimization.

📚 Practical Examples

Example 1: ML Pipeline with MLflow

Tracking: Set up MLflow tracking for experiments.

Pipeline: Create a pipeline (data → preprocessing → training → evaluation).

Registry: Save the model in MLflow Registry.

Deployment: Deploy through MLflow.

Example 2: CI/CD for ML

Tests: Create unit, integration, and model tests.

CI: Set up CI for automatic testing.

CD: Set up CD for automatic deployment.

Monitoring: Set up monitoring for production.

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MLOps: automation of the ML lifecycle