Accelerates the development of data pipelines, machine learning models, and modern AI agent architectures.
This skill equips Claude with specialized knowledge for architecting and implementing data engineering pipelines, production-grade ML systems, and advanced AI applications. It provides structured guidance across three distinct paths—Data Engineer, ML Engineer, and AI Engineer—offering curated technology stacks, learning milestones, and implementation patterns updated for 2025. Whether you are building distributed Spark jobs, fine-tuning PyTorch models, or orchestrating RAG systems with LangChain, this skill ensures best practices in scalability, observability, and reliability.
주요 기능
01Detailed architecture patterns for agentic loops and RAG systems
02Customized learning paths for Data, ML, and AI Engineering roles
03Troubleshooting frameworks for data drift, pipeline latency, and LLM hallucinations
041 GitHub stars
05Comprehensive algorithm and deep learning architecture references
06Production-ready 2025 technology stack recommendations including Spark, dbt, and LangChain
사용 사례
01Building production-ready AI agents with tool-calling and memory capabilities
02Designing scalable ETL/ELT pipelines using Airflow, dbt, and Snowflake
03Implementing MLOps workflows for model tracking and performance monitoring
