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Your ML & Data Knowledge Wiki. Best practices and expert-level knowledge for Machine Learning and Data Engineering, covering 1000+ frameworks and libraries from training to deployment.
Browse implementation patterns, configuration guides, debugging heuristics, and battle-tested defaults for frameworks like vLLM, DeepSpeed, Megatron-LM, FlashAttention, Triton, Unsloth, LangChain, and many more. Every page is structured so both humans and AI agents can find what they need fast.
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| Category | Description | Browse |
|---|---|---|
| Workflows | Step-by-step processes and procedures | Browse All |
| Principles | Core ideas and foundational knowledge | Browse All |
| Implementations | Code-level details and modules | Browse All |
| Heuristics | Best practices and guidelines | Browse All |
| Environments | Setup and configuration guides | Browse All |
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Workflows
- Workflow:Dagster io Dagster LLM Fine Tuning
- Workflow:Nautechsystems Nautilus trader Data loading and cataloging
- Workflow:Online ml River Online Clustering
- Workflow:Togethercomputer Together python Image Generation
- Workflow:Zai org CogVideo Video Editing DDIM Inversion
- Workflow:ArroyoSystems Arroyo SQL Pipeline Lifecycle
- Workflow:ContextualAI HALOs Online Iterative Alignment
- Workflow:Deepset ai Haystack Hybrid Document Search
- Workflow:Spcl Graph of thoughts GoT Document Merging Pipeline
- Workflow:Allenai Open instruct Reward Model Training
Principles
- Principle:Confident ai Deepeval Trace Metadata Enrichment
- Principle:Sktime Pytorch forecasting Tensor Utilities
- Principle:Arize ai Phoenix OTel Dependency Setup
- Principle:Microsoft Onnxruntime Memory Optimization
- Principle:Microsoft DeepSpeedExamples Inference Performance Measurement
- Principle:Langchain ai Langchain Package Scaffolding
- Principle:CARLA simulator Carla Unreal Engine Build
- Principle:Kubeflow Kubeflow Multi User Configuration
- Principle:Microsoft Autogen Swarm Orchestration
- Principle:CrewAIInc CrewAI Tool Assignment
Implementations
- Implementation:Neuml Txtai HFPipeline
- Implementation:Recommenders team Recommenders Benchmark Train Models
- Implementation:Run llama Llama index FunctionTool From Defaults
- Implementation:InternLM Lmdeploy Tensor
- Implementation:Predibase Lorax Triton LibEntry
- Implementation:Ollama Ollama MLXRunner MLX Generated C
- Implementation:Sdv dev SDV Evaluate Quality Multi Table
- Implementation:Huggingface Trl RewardTrainer Init Train
- Implementation:Infiniflow Ragflow Agent Constants
- Implementation:Haosulab ManiSkill Link
Heuristics
- Heuristic:OpenBMB UltraFeedback Principle Distribution Tuning
- Heuristic:Arize ai Phoenix Notebook Event Loop Patching
- Heuristic:HKUDS AI Trader Linear Retry Backoff
- Heuristic:Huggingface Open r1 Code Execution Timeout Strategy
- Heuristic:Facebookresearch Audiocraft FSDP Distributed Training Tips
- Heuristic:Vespa engine Vespa Document Batch Processing Strategy
- Heuristic:ARISE Initiative Robosuite XML Reset Method Tradeoff
- Heuristic:Googleapis Python genai API Retry Backoff Strategy
- Heuristic:EvolvingLMMs Lab Lmms eval Limit Flag Testing Only
- Heuristic:Iamhankai Forest of Thought Input Length Overflow Recovery
Environments
- Environment:Open compass VLMEvalKit Data Storage Environment
- Environment:Huggingface Datatrove Inference GPU Environment
- Environment:Dagster io Dagster Container Resource Monitoring
- Environment:PrefectHQ Prefect AI Integration Credentials
- Environment:Risingwavelabs Risingwave Rust Build Environment
- Environment:Cleanlab Cleanlab Python Core Environment
- Environment:Microsoft DeepSpeedExamples RLHF Training Environment
- Environment:Spotify Luigi AWS S3 Storage
- Environment:Mlc ai Mlc llm WebGPU Browser Environment
- Environment:Marker Inc Korea AutoRAG Python 3 10 Runtime