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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:OpenRLHF OpenRLHF Iterative DPO
- Workflow:Explodinggradients Ragas Agent Evaluation
- Workflow:AnswerDotAI RAGatouille Document Indexing And Search
- Workflow:Nightwatchjs Nightwatch E2E Test Authoring
- Workflow:Recommenders team Recommenders Neural Collaborative Filtering
- Workflow:Duckdb Duckdb Building From Source
- Workflow:Huggingface Optimum Automatic Tensor Parallelization
- Workflow:Promptfoo Promptfoo Red Team Security Scan
- Workflow:Cohere ai Cohere python Text Embedding
- Workflow:Huggingface Diffusers Text to Image Inference
Principles
- Principle:Openai Evals Eval Resolution
- Principle:Online ml River Drift Retraining
- Principle:AUTOMATIC1111 Stable diffusion webui Environment Reproducibility
- Principle:HKUDS AI Trader Web Search Integration
- Principle:Sail sg LongSpec Evaluation Callback Pipeline
- Principle:Kornia Kornia Edge Postprocessing
- Principle:Protectai Llm guard Observability Instrumentation
- Principle:Huggingface Optimum Framework Detection
- Principle:ClickHouse ClickHouse Third Party Dependency Management
- Principle:Mlc ai Web llm RAG Pipeline
Implementations
- Implementation:Bentoml BentoML Gradio Mount
- Implementation:Mage ai Mage ai Destination Process
- Implementation:Huggingface Trl TrlParser SFTConfig
- Implementation:Online ml River Optim Newton
- Implementation:Allenai Open instruct AutoModelForSequenceClassification From Pretrained
- Implementation:Cohere ai Cohere python ApiMetaBilledUnits Model
- Implementation:Langgenius Dify UseTriggers
- Implementation:Tensorflow Serving Resnet K8s Manifest
- Implementation:PacktPublishing LLM Engineers Handbook Reranker Generate
- Implementation:Hpcaitech ColossalAI Zero Bubble GRPOConsumer
Heuristics
- Heuristic:Sail sg LongSpec TF32 And FP16 Training
- Heuristic:Duckdb Duckdb Test Development Guidelines
- Heuristic:Alibaba MNN Warning Deprecated Winograd Codegen
- Heuristic:Sktime Pytorch forecasting Batch Size Selection
- Heuristic:Mage ai Mage ai HTTP Request Timeout Defaults
- Heuristic:Mlc ai Mlc llm Engine Mode Selection
- Heuristic:Neuml Txtai Faiss Index Sizing Tip
- Heuristic:Junyanz Pytorch CycleGAN and pix2pix Batch Size One Default
- Heuristic:Mage ai Mage ai Parallel Sink Concurrency Limit
- Heuristic:PrefectHQ Prefect Retry Backoff Strategy
Environments
- Environment:Vespa engine Vespa CMake Cpp23 Build Environment
- Environment:Pola rs Polars GPU Execution Environment
- Environment:Mlfoundations Open flamingo Evaluation Dependencies
- Environment:Duckdb Duckdb Extension Distribution Env
- Environment:Shiyu coder Kronos Qlib Data Environment
- Environment:Apache Dolphinscheduler ZooKeeper Registry
- Environment:LMCache LMCache Python Runtime
- Environment:Dotnet Machinelearning OneDal Acceleration
- Environment:Kserve Kserve Istio Service Mesh
- Environment:Mage ai Mage ai Python 3 9 Runtime