LLM Zoomcamp 2026
Create practical LLM applications with search, retrieval, RAG, evaluation, monitoring, and production workflows for AI systems.
Jun 8, 2026 - Aug 17, 2026 (10 weeks) GitHub
Community-created courses with practical homework, projects, public leaderboards, and peer review.
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Courses listed here are open for participation and assignment submissions.
Create practical LLM applications with search, retrieval, RAG, evaluation, monitoring, and production workflows for AI systems.
Jun 8, 2026 - Aug 17, 2026 (10 weeks) GitHub
Build production-style data pipelines with Docker, workflow orchestration, data warehouses, analytics engineering, batch processing, streaming, and a final project.
Jan 12, 2026 - May 11, 2026 (17 weeks) GitHub
Last assignment
Peer review: Last Project Attempt
Due May 11, 2026
Past cohorts remain open for self check; late submissions are not possible.
Use AI coding tools in real development workflows: prompting, agents, testing, code review, and shipping production-quality software faster.
A hands-on path through machine learning engineering: regression, classification, trees, deep learning, serverless inference, Kubernetes, and a capstone project.
Create practical LLM applications with search, retrieval, RAG, evaluation, monitoring, and production workflows for AI systems.
Analyze stock market data with Python, SQL, and modern data tools: data collection, indicators, dashboards, and trading-oriented analysis.
Productionize machine learning systems with experiment tracking, orchestration, deployment, monitoring, and reliable ML workflows.
Build production-style data pipelines with Docker, workflow orchestration, data warehouses, analytics engineering, batch processing, streaming, and a final project.
A hands-on path through machine learning engineering: regression, classification, trees, deep learning, serverless inference, Kubernetes, and a capstone project.
Create practical LLM applications with search, retrieval, RAG, evaluation, monitoring, and production workflows for AI systems.
Productionize machine learning systems with experiment tracking, orchestration, deployment, monitoring, and reliable ML workflows.
Analyze stock market data with Python, SQL, and modern data tools: data collection, indicators, dashboards, and trading-oriented analysis.
Build production-style data pipelines with Docker, workflow orchestration, data warehouses, analytics engineering, batch processing, streaming, and a final project.