Design and deploy ML models with reproducible training pipelines and monitoring.
npx skills add https://github.com/asalhamed/dev-agents --skill ml-engineer-asalhamed
Please help me install this Agent Skill. Skill: ml-engineer Source: https://github.com/asalhamed/dev-agents/tree/main/ml-engineer Command: npx skills add https://github.com/asalhamed/dev-agents --skill ml-engineer-asalhamed
Design and deploy robust ML models by bridging research and production, ensuring reproducibility and scalable deployment.
Set up a reproducible ML project that defines problem, data schema, training plan, deployment target, and monitoring strategy.
High-intent search queries and answers about installing and using this skill.
Reproducible ML training pipelines require versioned data, fixed seeds, and experiment tracking to ensure consistent results before model deployment. This workflow supports MLflow-style logging and model registry integration to bridge research and production.
Exporting ML models for ONNX and TorchServe deployment involves converting trained artifacts into standard formats for scalable inference. This ensures models are portable across cloud and edge targets while maintaining explicit input and output schemas.
Model monitoring and drift detection in MLOps pipelines track performance degradation and data shifts post-deployment. This process evaluates incoming inference data against baseline distributions to trigger alerts when model behavior diverges from training expectations.
Yes, this approach supports both cloud and edge ML deployments by defining explicit deployment targets early in the project lifecycle. It accommodates diverse architectures including predictive modeling, anomaly detection, and computer vision tasks.
ML engineering tasks require explicit input and output contracts, defining data schemas and privacy considerations before training begins. This structured framing ensures reproducible pipelines and compliant model deployment across predictive modeling and monitoring workflows.
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