Descubre Habilidades de Claude para data science & ml. Explora 53 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Builds production-ready Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground AI responses in external data.
Architects sophisticated LLM applications using LangChain with advanced agent patterns, persistent memory, and modular tool integrations.
Implements robust Retrieval-Augmented Generation (RAG) systems for LLM applications using vector databases and semantic search.
Master advanced techniques to maximize LLM performance, reliability, and controllability in production environments.
Orchestrates end-to-end MLOps pipelines from data preparation and training to validation and production deployment.
Implement comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and benchmarking.
Orchestrates end-to-end machine learning lifecycles from data preparation and training to production deployment and monitoring.
Designs and implements sophisticated LLM applications using the LangChain framework with advanced patterns for agents, memory, and tool integration.
Implements advanced prompt engineering techniques to optimize LLM performance, reliability, and controllability in production environments.
Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and comparative benchmarking.
Orchestrates end-to-end machine learning pipelines from automated data preparation through model training, validation, and production deployment.
Streamlines the development of PySpark ETL pipelines and distributed data processing workflows.
Implements production-grade computer vision systems including object detection, segmentation, and real-time video processing using industry-standard frameworks.
Implement advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production environments.
Builds sophisticated LLM-powered applications using autonomous agents, complex chains, and context-aware memory systems.
Facilitates the configuration, performance optimization, and operational management of Templar AI miners within the Bittensor decentralized training network.
Queries the China Biographical Database to retrieve detailed biographical information, social relationships, and official appointments of historical Chinese figures from the 7th century BCE through the 19th century CE.
Builds and orchestrates end-to-end MLOps pipelines from data preparation through model training to production deployment.
Iteratively improves LA-Bench experimental procedures through automated validation, regeneration, and data persistence cycles.
Extracts structured experimental protocol data and instructions from LA-Bench format JSONL files.
Generates sophisticated, interactive data visualizations and custom SVG graphics with precise control over visual elements and transitions.
Automates scientific data analysis and graphing workflows using GraphPad Prism scripting and XML manipulation.
Designs, optimizes, and implements robust Apache Airflow DAGs using industry-standard operators and scheduling best practices.
Orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.
Builds robust Retrieval-Augmented Generation (RAG) systems to ground LLM applications in external knowledge and proprietary data.
Implements comprehensive evaluation strategies for Large Language Model applications using automated metrics, human feedback, and comparative benchmarking.
Builds sophisticated LLM applications and autonomous agents using the LangChain framework's core patterns and integrations.
Implements advanced prompt engineering techniques to optimize LLM performance and output reliability for production applications.
Architects sophisticated LLM applications using LangChain patterns for agents, memory management, and complex workflow orchestration.
Implements comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and LLM-as-judge patterns.
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