Senior Ml Engineer
Skill Verifiziert AktivML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.
To provide ML engineers with robust patterns and tools for deploying, monitoring, and managing ML models and LLM integrations in production environments.
Funktionen
- Model deployment workflows and containerization
- MLOps pipeline setup with feature stores and experiment tracking
- LLM integration with retry logic and cost controls
- RAG system implementation guidance
- Model monitoring for drift and performance degradation
Anwendungsfälle
- Deploying trained ML models to production environments.
- Setting up automated MLOps infrastructure (MLflow, Kubeflow, Kubernetes).
- Integrating LLM APIs into applications with advanced controls.
- Building and optimizing Retrieval-Augmented Generation (RAG) systems.
- Monitoring model performance and detecting data/model drift.
Nicht-Ziele
- Initial model research and development.
- Model training script generation.
- Abstracting away core MLOps tooling entirely (provides patterns, not a black box).
Installation
Zuerst Marketplace hinzufügen
/plugin marketplace add alirezarezvani/claude-skills/plugin install engineering-team@claude-code-skillsQualitätspunktzahl
VerifiziertVertrauenssignale
Ähnliche Erweiterungen
Chat Format
100Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval
Rag Architect
100Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.
TimesFM Forecasting
100Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
Azure Monitor Query Py
100Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics. Triggers: "azure-monitor-query", "LogsQueryClient", "MetricsQueryClient", "Log Analytics", "Kusto queries", "Azure metrics".
Embedding Strategies
100Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Build Feature Store
99Build a feature store using Feast for centralized feature management, configure offline and online stores for batch and real-time serving, define feature views with transformations, and implement point-in-time correct joins for ML pipelines. Use when managing features for multiple ML models, ensuring training-serving consistency, serving low-latency features for real-time inference, reusing feature definitions across projects, or building a feature catalog for discovery and governance.