Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Builds sophisticated LLM applications using LangChain for prompt management, model chaining, and structured output parsing.
Architects and optimizes LLM-powered applications using structured methodologies, pipeline design, and agent-assisted development patterns.
Optimizes large language models for efficient inference and training by reducing memory footprint using advanced precision-shifting techniques like 4-bit and 8-bit quantization.
Transforms external RDF context into formal Belief-Desire-Intention (BDI) models to enable rational agency and explainable reasoning in AI agents.
Initializes a standardized project structure for financial underwriting and quant development using the DRIVER methodology.
Fine-tunes vision-language models like Pixtral and Ministral using Unsloth's FastVisionModel optimizations for faster training.
Optimizes Large Language Models using Direct Preference Optimization to align behavior with preferred response pairs without explicit reward modeling.
Streamlines parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and Unsloth to optimize memory and training speed.
Implements Group Relative Policy Optimization for efficient LLM alignment and reinforcement learning from human feedback.
Performs automated exploratory data analysis and generates comprehensive reports for over 200 scientific file formats.
Orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.
Provides foundational expertise in context engineering to optimize AI agent performance and manage token usage effectively.
Provides a clean, Pythonic interface for local LLM inference, chat completions, and model management using the official Ollama library.
Designs and implements sophisticated multi-agent systems using supervisor, swarm, and hierarchical patterns to solve complex context management challenges.
Diagnoses and mitigates AI agent performance failures caused by long-context attention loss, poisoning, and informational clash.
Builds high-quality fine-tuning datasets from literary works to train AI models in specific authorial voices and writing styles.
Optimizes LLM fine-tuning via advanced QLoRA patterns, hyperparameter tuning, and memory-efficient implementation strategies.
Guides the development of high-performance ML and AI applications in Rust using memory-efficient patterns and GPU acceleration.
Optimizes vector database performance by balancing search latency, recall accuracy, and memory footprint.
Provides a comprehensive framework and guidance for building professional finance and quantitative analysis tools with AI assistance.
Manages ComfyUI instances for node-based Stable Diffusion image generation with automated GPU configuration and model management.
Manages FiftyOne dataset visualization and curation environments using Podman Quadlet containers with integrated MongoDB sidecars.
Enforces E8 architecture standards and QIG purity protocols within the Pantheon development ecosystem.
Facilitates the use of local Ollama models with the official OpenAI Python library and compatible AI orchestration frameworks.
Manages multi-instance JupyterLab environments with hardware-accelerated GPU support via Podman Quadlet.
Builds sophisticated LLM-powered applications using autonomous agents, complex chains, and context-aware memory systems.
Implements end-to-end machine learning pipelines in R using the modern tidymodels ecosystem.
Quantifies hedge fund capital flows in agricultural commodity markets using CFTC COT data and macro sentiment indicators.
Manages local LLM inference using Ollama and Podman Quadlet with full GPU acceleration support.
Streamlines computational molecular biology tasks including sequence analysis, biological file parsing, and genomic database integration.
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