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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 |
Explore Pages
Workflows
- Workflow:Treeverse LakeFS Garbage Collection
- Workflow:Haotian liu LLaVA Single Image Inference
- Workflow:Open compass VLMEvalKit Adding Custom VLM
- Workflow:Iterative Dvc Experiment Tracking
- Workflow:Dotnet Machinelearning Text Classification
- Workflow:DataTalksClub Data engineering zoomcamp dlt Data Ingestion
- Workflow:ChenghaoMou Text dedup MinHash LSH Deduplication
- Workflow:Apache Hudi Flink Batch Incremental Read
- Workflow:Haotian liu LLaVA Web Demo Deployment
- Workflow:Mlflow Mlflow GenAI Evaluation
Principles
- Principle:Neuml Txtai Model Fine Tuning
- Principle:Duckdb Duckdb Third Party Dependency Compilation
- Principle:Openai Whisper Segment Assembly
- Principle:Infiniflow Ragflow File Management
- Principle:ClickHouse ClickHouse RAII Scope Guard
- Principle:Google deepmind Mujoco Restrict Optimization
- Principle:Microsoft Playwright Configure Trace Collection
- Principle:Pytorch Serve Multimodal Inference
- Principle:Cohere ai Cohere python Tool Result Submission
- Principle:SqueezeAILab ETS Policy Model Serving
Implementations
- Implementation:Google deepmind Mujoco Engine Util Misc
- Implementation:Triton inference server Server GenQaModels
- Implementation:LaurentMazare Tch rs Imagenet Top
- Implementation:Recommenders team Recommenders Notebook Utils
- Implementation:Turboderp org Exllamav2 Tokenizer Encode Multimodal
- Implementation:Open compass VLMEvalKit CMMMU
- Implementation:Apache Paimon JsonSerdeUtil
- Implementation:Datahub project Datahub Check Docker Quickstart
- Implementation:Recommenders team Recommenders NAML Model
- Implementation:Apache Druid DestinationForm
Heuristics
- Heuristic:Hiyouga LLaMA Factory Mixed Precision Training Tips
- Heuristic:Hpcaitech ColossalAI CUDA Device Max Connections Tip
- Heuristic:Puppeteer Puppeteer Mac Silicon Performance
- Heuristic:Ray project Ray Serve Concurrency And Backpressure
- Heuristic:HKUDS AI Trader Sortino Ratio Capping
- Heuristic:Fede1024 Rust rdkafka Cooperative Rebalance Protocol
- Heuristic:PrefectHQ Prefect SQLite Performance Tuning
- Heuristic:Trailofbits Fickling Severity Threshold Selection
- Heuristic:CarperAI Trlx Reward Model Layer Freezing
- Heuristic:Openai Whisper Compression Ratio Threshold
Environments
- Environment:Apache Spark Kubernetes Runtime
- Environment:EvolvingLMMs Lab Lmms eval Server Mode Environment
- Environment:Protectai Modelscan TensorFlow Optional
- Environment:Mbzuai oryx Awesome LLM Post training Python Requests
- Environment:Apache Beam Portable Runner Environment
- Environment:Ucbepic Docetl Python Runtime
- Environment:Bitsandbytes foundation Bitsandbytes Build From Source Environment
- Environment:Microsoft Semantic kernel ONNX CUDA Environment
- Environment:Fede1024 Rust rdkafka CI Test Runner
- Environment:Promptfoo Promptfoo Provider API Keys