Skill UI

Mastering the full lifecycle of machine learning models from data ingestion to deployment. This expertise covers building robust, production-grade MLOps pipelines using orchestrators like Kubeflow, Airflow, or Prefect. Key skills include feature store implementation with Feast, rigorous data validation, experiment tracking (e.g., MLflow), model registry management, and automated deployment workflows. Essential for automating model training and ensuring reproducibility at scale.

This skill provides comprehensive guidance for building production-grade MLOps pipelines. It covers the entire machine learning lifecycle, from initial data ingestion and rigorous feature engineering to model training, advanced validation (A/B testing, performance drift), and robust deployment strategies (Canary, Blue-green). It enables the design and orchestration of scalable, reproducible, and observable ML systems using tools like Airflow and Kubeflow.

This comprehensive guide covers the entire MLOps lifecycle, specializing in building robust, scalable, and production-grade Machine Learning systems. Expertise includes orchestrating complex ML pipelines using Kubeflow, Airflow, Prefect, and cloud-native services (AWS SageMaker, Azure ML, GCP Vertex AI). It covers experiment tracking (MLflow, W&B), model versioning, model registries, infrastructure as code (Terraform), and container deployment using Kubernetes. Ideal for engineers tasked with moving ML models from research to reliable, managed production environments.

Automates guidance for evaluating machine learning models, covering best practices, sample code, and validation patterns triggered by mentions of model evaluation metrics.

Automates guidance for deploying machine learning models with quantization, covering tooling, best practices, and production readiness for inference, MLops, and monitoring tasks.

Prediction Monitor Assistant automates machine learning deployment monitoring tasks, offering step-by-step guidance, best-practice patterns, production-ready code and configuration generation, and validation to keep serving and inference workflows reliable.

This skill builds and executes automated pipelines to clean, validate, and transform data for machine learning, covering ETL workflows, error handling, and quality reporting so Claude can deliver analysis-ready datasets.

PyTorch Lightning is a high-level framework built on PyTorch that significantly reduces boilerplate code in deep learning training loops. It automatically handles device management, distributed training (DDP, FSDP, DeepSpeed), mixed precision, and advanced callbacks. It allows researchers to scale models from local machines to supercomputers with minimal code changes, enabling clean, scalable, and best-practice deep learning implementations.

This skill covers the full lifecycle of deploying machine learning models into production environments. It focuses on establishing robust MLOps pipelines, including model deployment workflows (e.g., Docker, Canary releases), feature store management (Feast), and comprehensive drift monitoring. Furthermore, it details advanced integration patterns for Large Language Models (LLMs) and building Retrieval-Augmented Generation (RAG) systems, emphasizing operational stability, performance validation, and cost optimization.

Automates splitting datasets into training, validation, and test subsets by generating and running Python code so you can quickly prepare data for machine learning model training and evaluation.

Automates the end-to-end training of machine learning models by analyzing datasets, selecting suitable algorithms, tuning parameters, performing cross-validated training, and emitting performance metrics plus a saved model artifact.

Guides Claude to automatically tune machine learning model hyperparameters via grid, random, or Bayesian search, generating runnable Python code, executing validations, and reporting best configurations plus performance metrics for accuracy or other goals.