🤖AI & ML
Ship AI features, not science experiments
Model training pipelines, LLM tooling, prompt engineering frameworks, and ML observability.
What a ai & ml stack contains
Model pipeline scaffold
LLM integration toolkit
Prompt management
Evaluation framework
ML observability
Example use cases
LLM app
OpenAI + LangChain + prompt versioning + eval suite.
ML pipeline
Training + model registry + serving + monitoring.
AI features
RAG pipeline + vector store + semantic search.
Who publishes these stacks?
ML engineers and AI product builders who have shipped AI features in production.