Guide MLOps practices with Python code for FastAPI, MLflow, and Prometheus.
npx skills add https://github.com/hyukudan/ai-skills --skill ml-ops-hyukudan
Please help me install this Agent Skill. Skill: ml-ops Source: https://github.com/hyukudan/ai-skills/tree/main/examples/skills/ml-ops Command: npx skills add https://github.com/hyukudan/ai-skills --skill ml-ops-hyukudan
This Skill provides comprehensive guidance and code examples for deploying, monitoring, and managing Machine Learning and Large Language Model systems in production environments.
Use the ml-ops skill to deploy a FastAPI model server for text classification.
High-intent search queries and answers about installing and using this skill.
Deploy a FastAPI model serving API by using the provided Python code snippets to create REST endpoints for text classification. This sets up robust serving patterns for ML systems in production environments.
Set up CI/CD pipelines for ML model deployment by using the included YAML examples for GitHub Actions. These automate the ML lifecycle including validation, training, testing, and deployment stages.
Data drift detection in production ML systems works by tracking metrics via Prometheus and monitoring performance degradation. The provided code helps set up observability to alert when input data distributions shift.
You can use MLflow for model registry and versioning. The Skill provides Python code for MLflow integration to track and manage model versions throughout the deployment lifecycle.
The best way to run A/B testing for different model variants is using the provided A/B testing frameworks. These frameworks help experiment with different models and assess statistical significance of results.
You need Prometheus or similar monitoring tools to track ML pipeline metrics effectively. The Skill includes Python code for Prometheus metrics to set up observability and detect performance degradation.
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