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Found 19 Skills
Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Auto-generate features with encodings, scaling, polynomial features, and interaction terms for ML pipelines.
Principal backend engineering intelligence for Python AI/ML systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale ML services and pipelines. Focus: data quality, reproducibility, reliability, performance, security, observability, model evaluation, MLOps.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
Expert MLOps engineering covering model deployment, ML pipelines, model monitoring, feature stores, and infrastructure automation.
Sklearn Pipeline Builder - Auto-activating skill for ML Training. Triggers on: sklearn pipeline builder, sklearn pipeline builder Part of the ML Training skill category.