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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:Run llama Llama index Evaluation Pipeline
- Workflow:Protectai Llm guard Scanner Benchmarking
- Workflow:Microsoft LoRA LoRA Integration
- Workflow:Predibase Lorax Multi Adapter Merging
- Workflow:Deepspeedai DeepSpeed Pipeline Parallel Training
- Workflow:Openai Openai node Chat Completion
- Workflow:Testtimescaling Testtimescaling github io GitHub Pages Course Progression
- Workflow:LaurentMazare Tch rs LLM Text Generation
- Workflow:Microsoft Autogen Tool Augmented Agent
- Workflow:DistrictDataLabs Yellowbrick Regression Model Evaluation
Principles
- Principle:Microsoft Autogen State Persistence
- Principle:Pytorch Serve Neuron Accelerated Inference
- Principle:Volcengine Verl Multi Turn Data Preparation
- Principle:Ggml org Llama cpp GGUFFormat
- Principle:SeldonIO Seldon core Pipeline Version Progression
- Principle:PeterL1n BackgroundMattingV2 TorchScript export
- Principle:Kubeflow Pipelines Model Evaluation Metrics
- Principle:Microsoft LoRA LoRA Checkpoint Evaluation
- Principle:Run llama Llama index Query Engine Creation
- Principle:Datahub project Datahub Entity Metadata Enrichment
Implementations
- Implementation:Nightwatchjs Nightwatch Custom Command Type Definitions
- Implementation:Farama Foundation Gymnasium Tuple Space
- Implementation:Microsoft Onnxruntime ORTModule Training Execution
- Implementation:Google deepmind Mujoco MJWarp Island
- Implementation:Run llama Llama index MistralAIFinetuneEngine
- Implementation:Neuml Txtai SIF Scoring
- Implementation:Explodinggradients Ragas LlamaIndex Integration
- Implementation:Astronomer Astronomer cosmos MemoryTracker
- Implementation:Google deepmind Mujoco Engine Util Sparse Header
- Implementation:Neuml Txtai SBert Vectors
Heuristics
- Heuristic:Bigscience workshop Petals Batch Splitting Threshold
- Heuristic:Lucidrains X transformers Numerical Stability Techniques
- Heuristic:Treeverse LakeFS S3 Multipart Size Constraint
- Heuristic:Unslothai Unsloth Gradient Accumulation Accuracy
- Heuristic:Apache Flink False Positive Availability Optimization
- Heuristic:Huggingface Datatrove Thundering Herd Prevention
- Heuristic:Huggingface Datasets Flatten Indices Performance
- Heuristic:Kserve Kserve NCCL RoCE Auto Detection
- Heuristic:Microsoft Semantic kernel Prompt Injection Safety
- Heuristic:Norrrrrrr lyn WAInjectBench LoRA Rank Alpha Selection
Environments
- Environment:Datahub project Datahub Frontend Build
- Environment:FMInference FlexLLMGen HuggingFace Access
- Environment:Sgl project Sglang Python Dependencies
- Environment:Marker Inc Korea AutoRAG GPU PyTorch Environment
- Environment:Datahub project Datahub Spark Lineage Environment
- Environment:Ray project Ray CI Build Matrix Environment
- Environment:Openai Openai agents python MCP Dependencies
- Environment:Haosulab ManiSkill Python SAPIEN Core
- Environment:Openclaw Openclaw Mintlify Documentation Platform
- Environment:Apache Shardingsphere Java Runtime Environment