Automate production ML lifecycle design, deployment, and monitoring.
npx skills add https://github.com/melikhanmutlu/web_ar --skill ml-engineer-melikhanmutlu
Please help me install this Agent Skill. Skill: ml-engineer Source: https://github.com/melikhanmutlu/web_ar/tree/main/skills-extra/ml-engineer Command: npx skills add https://github.com/melikhanmutlu/web_ar --skill ml-engineer-melikhanmutlu
Automates the end-to-end lifecycle of production ML systems (design, deployment, and monitoring) for reliable inference at scale.
Define your production ML workload and follow the setup steps to deploy a complete ML pipeline.
High-intent search queries and answers about installing and using this skill.
You can automate the end-to-end lifecycle of production ML systems by defining your workload to deploy a complete pipeline. This Skill handles scalable design, deployment, and monitoring of serving architectures, feature stores, and observability dashboards.
Yes, production-grade model serving supports PyTorch 2.x, TensorFlow 2.x, and ONNX. It enables reliable inference at scale across cloud or on-prem deployments while integrating feature engineering and infrastructure monitoring.
The best way to set up observability and A/B testing for ML deployments is to integrate them directly into your automated pipeline. This Skill maintains performance and compliance through automated monitoring, alerts, and governance dashboards.
Yes, you can build feature stores and data pipelines for on-prem or cloud environments. This Skill applies feature engineering and data processing to establish reliable data pipelines for production ML systems.
You deploy a real-time inference service for fraud-detection models by defining your production ML workload and following the setup steps. This deploys a complete pipeline with automated monitoring, alerts, and observability dashboards.
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