Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Writes dataframe-agnostic Python code that runs seamlessly across pandas, Polars, PyArrow, and other major backends.
Validates agent YAML frontmatter and configurations to ensure compatibility with ML Odyssey requirements.
Verifies complete agent system coverage across hierarchy levels, development phases, and project sections.
Analyzes Mojo source code to identify and implement SIMD optimization opportunities for high-performance tensor and array operations.
Simplifies LLM API integration by providing a unified Python interface for over 100 providers using a consistent OpenAI-compatible format.
Develops and deploys in-database machine learning models using the SAP HANA Python Client for PAL, APL, and AutoML workflows.
Integrates SAP AI Core and Generative AI Hub capabilities into JavaScript and Java applications with enterprise-grade orchestration and security features.
Builds and optimizes data processing pipelines, integrations, and machine learning scenarios within the SAP Data Intelligence Cloud environment.
Orchestrates end-to-end machine learning workflows using industry-standard tools like Airflow, Kubeflow, and MLflow.
Simplifies the installation, configuration, and management of Mozilla Llamafile for running local, OpenAI-compatible LLMs.
Deploys production-ready recommendation architectures featuring multi-tier caching, feature stores, and automated A/B testing frameworks.
Deploys and manages enterprise AI/ML workloads, Generative AI Hub models, and orchestration pipelines on SAP BTP.
Optimizes Haystack RAG pipelines by leveraging DSPy's data-driven prompt tuning and programmatic optimization capabilities.
Creates, analyzes, and manages complex Excel spreadsheets with a focus on financial modeling standards and formula integrity.
Transforms vague human intent into structured, production-ready prompt artifacts through iterative clarification.
Conducts deep-dive information gathering and data synthesis to produce actionable strategic insights and comprehensive reports.
Configures and manages local LLM inference using Mozilla's llamafile to provide offline, OpenAI-compatible AI capabilities.
Simplifies LLM integration by providing a consistent OpenAI-style Python interface for over 100 cloud and local providers.
Optimizes AI prompts using research-backed frameworks and production-ready templates to ensure high-quality, cost-effective model outputs.
Provides comprehensive technical guidance on Reinforcement Learning from Human Feedback for aligning large language models with human preferences.
Loads and deploys state-of-the-art pretrained models for text, vision, and audio using the Hugging Face library.
Optimizes LLM output quality by providing domain-specific guidance and structural patterns for crafting high-performance prompts.
Architects sophisticated AI agent systems and LLM workflows using proven patterns like ReAct, prompt chaining, and orchestrator-worker models.
Optimizes and runs large language models on Apple Silicon using the native MLX framework for high-performance inference and fine-tuning.
Integrates Hyperspell’s long-term memory and RAG capabilities into your project with automated configuration and SDK setup.
Executes machine learning examples on remote GPU hosts via SSH by syncing minimal workspaces and launching Docker-based training scripts.
Integrates large language models with the emotive-mascot engine to create sentiment-driven, emotionally responsive conversational interfaces.
Integrates AI-powered qualitative analysis into Python pandas workflows for intelligent data sorting, deduplication, and merging.
Maps LinkML enum permissible values to verified ontology terms and CURIEs using the Ontology Access Kit (OAK).
Implements robust pipes-and-filters architectures for complex ETL, media processing, and data transformation workloads.
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