moai-domain-ml

Automate end-to-end ML pipelines by orchestrating data preprocessing, model training, evaluation, and deployment steps across TensorFlow, PyTorch, and scikit-learn. This Skill codifies enterprise patterns for scalable workflows, including experiment tracking, model validation, and production serving via ONNX, TensorFlow Serving, TorchServe, and Seldon Core.

Yes. This Skill supports cross-framework ML pipelines that work with TensorFlow, PyTorch, and scikit-learn, enabling data science teams to standardize workflows while switching models between frameworks without rewriting pipeline logic.

Enterprise ML pipelines require experiment tracking to log model versions and metrics, deployment patterns for reproducible releases, model monitoring to detect performance drift, and automated retraining workflows. This Skill integrates these MLOps functions into a single production-ready system.

Implement model lifecycle management through built-in monitoring to track performance metrics, validation rules to detect degradation, and automated retraining pipelines triggered by performance thresholds—ensuring deployed models stay accurate without manual intervention.

No. This Skill unifies experiment tracking, model lifecycle management, and deployment orchestration in a single framework, reducing tool sprawl and configuration overhead for data science teams scaling ML to production.

Start with a simple ML pipeline: define data processing steps, add model training and evaluation, then deploy using the integrated patterns for TensorFlow Serving or TorchServe. The Skill handles infrastructure abstraction so teams focus on model logic.