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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:Vibrantlabsai Ragas Experiment Driven Development
- Workflow:MarketSquare Robotframework browser Browser Test Authoring
- Workflow:Sgl project Sglang ModelOpt Quantization And Export
- Workflow:Mlc ai Web llm Web Worker Deployment
- Workflow:Langchain ai Langgraph CLI Deployment
- Workflow:MarketSquare Robotframework browser JavaScript Extension
- Workflow:Vibrantlabsai Ragas Prompt Optimization
- Workflow:Microsoft Playwright Browser automation CLI
- Workflow:AnswerDotAI RAGatouille ColBERT Training
- Workflow:Lakeraai Pint benchmark Custom System Evaluation
Principles
- Principle:Scikit learn contrib Imbalanced learn SVM Borderline Oversampling
- Principle:SeldonIO Seldon core Usage Metrics Data Model
- Principle:LMCache LMCache Worker Registration
- Principle:FMInference FlexLLMGen Distributed Process Launching
- Principle:Lucidrains X transformers Variational Latent Language Modeling
- Principle:OpenHands OpenHands Auth Setup
- Principle:Togethercomputer Together python Batch Job Creation
- Principle:Dotnet Machinelearning Segment Encoding Optimization
- Principle:Huggingface Transformers Trainer Initialization
- Principle:Fastai Fastbook Data Collection
Implementations
- Implementation:Datajuicer Data juicer CalibrateQAMapper Process
- Implementation:Apache Druid InputFormatStep
- Implementation:Open compass VLMEvalKit Result Transfer
- Implementation:Scikit learn Scikit learn DatasetsModule
- Implementation:Triton inference server Server GenQaDynaSequenceImplicitModels
- Implementation:Tencent Ncnn Stb Image
- Implementation:TobikoData Sqlmesh Errors Page
- Implementation:FlowiseAI Flowise EvaluationResultVersionsSideDrawer
- Implementation:Pola rs Polars CategoricalPhysical
- Implementation:Pyro ppl Pyro ImproperUniform
Heuristics
- Heuristic:Gretelai Gretel synthetics GPU Memory Allow Growth
- Heuristic:Apache Hudi Compaction Scheduling Safety
- Heuristic:Mage ai Mage ai Sorted Data Bookmark Strategy
- Heuristic:OpenBMB UltraFeedback GPU Memory Utilization
- Heuristic:NVIDIA DALI Warning Deprecated C API V1 Functions
- Heuristic:Microsoft Semantic kernel Telemetry Log Level Configuration
- Heuristic:Huggingface Alignment handbook Liger Kernel Memory
- Heuristic:Testtimescaling Testtimescaling github io Hardcoded IDs vs Registry
- Heuristic:OpenBMB UltraFeedback API Retry Strategy
- Heuristic:Haosulab ManiSkill Initial Pose Performance
Environments
- Environment:FMInference FlexLLMGen HuggingFace Access
- Environment:Vllm project Vllm CUDA GPU Runtime
- Environment:ChenghaoMou Text dedup Python 3 12 Environment
- Environment:Treeverse LakeFS Web UI Environment
- Environment:Mlc ai Mlc llm WebGPU Browser Environment
- Environment:Recommenders team Recommenders Python Core Dependencies
- Environment:MaterializeInc Materialize Python CI Builder Runtime
- Environment:Microsoft DeepSpeedExamples SuperOffload Runtime
- Environment:Pola rs Polars Python Runtime Environment
- Environment:BerriAI Litellm Redis Cache Backend