ai-ml-ops

What problem does it solve?

This Skill streamlines the entire machine learning model lifecycle, from development and deployment to monitoring and optimization, ensuring production-grade MLOps practices.

Core Features & Use Cases

• Experiment Tracking: Log parameters, metrics, and artifacts using tools like MLflow.
• Model Registry: Manage model versions and stages (Staging, Production).
• Feature Stores: Ensure training/serving consistency with tools like Feast.
• Model Serving: Deploy models for real-time, batch, or streaming inference.
• Monitoring: Detect data drift, performance degradation, and system issues.
• Pipelines: Orchestrate complex ML workflows with Kubeflow or Airflow.
• A/B Testing: Implement strategies like canary releases and A/B tests.
• Explainability: Understand model decisions with SHAP and LIME.
• Use Case: Deploying a fraud detection model involves tracking experiments, registering validated models, serving predictions with low latency, monitoring for drift, and ensuring fairness.

Quick Start

Use the ai-ml-ops skill to set up experiment tracking for a new NLP model.