mlops-expert

What problem does it solve?

This Skill provides expert-level MLOps solutions for setting up and monitoring ML pipelines in production, including experiment tracking, model registry, serving, feature stores, and more.

Core Features & Use Cases

• Experiment Tracking: Utilizes MLflow for tracking experiments, logging metrics, and versioning models.
• Model Registry: Manages model versions through stages, from training to production.
• Model Serving: Implements model serving with FastAPI and MLflow models.
• Feature Stores: Incorporates feature stores like Feast for online/offline feature serving.
• Data Versioning: Uses DVC for data versioning and reproducibility.
• CI/CD for ML: Automates training pipelines and validation gates with CI/CD pipelines.
• Model Monitoring: Detects data and concept drift, monitors predictions, and alerts.

Quick Start

Use the mlops-expert skill to set up an ML pipeline for your project with the following command: setup-ml-pipeline -d /path/to/dataset -m /path/to/model.