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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:BerriAI Litellm Observability Integration
- Workflow:Run llama Llama index OpenAI LLM Finetuning
- Workflow:ThreeSR Awesome Inference Time Scaling Manual Paper Contribution
- Workflow:NVIDIA NeMo Curator Fuzzy Deduplication
- Workflow:Dotnet Machinelearning Text Classification
- Workflow:PeterL1n BackgroundMattingV2 Video matting inference
- Workflow:Haifengl Smile SQL Analytics Pipeline
- Workflow:Ggml org Ggml Vision Model Inference
- Workflow:OpenGVLab InternVL Preference Optimization
- Workflow:Open compass VLMEvalKit Video Benchmark Evaluation
Principles
- Principle:Triton inference server Server Stress Testing
- Principle:Alibaba ROLL DPO Loss Computation
- Principle:Apache Paimon Developer Tooling
- Principle:Iamhankai Forest of Thought LLM as Judge
- Principle:Apache Kafka Coordinator Partition Unloading
- Principle:Mbzuai oryx Awesome LLM Post training Keyword Data Loading
- Principle:Sdv dev SDV Sequential Data Sampling
- Principle:Allenai Open instruct Supervised Finetuning
- Principle:Promptfoo Promptfoo Assertion Grading
- Principle:Princeton nlp Tree of thought llm BFS Tree Search
Implementations
- Implementation:Getgauge Taiko Intercept Redirect
- Implementation:Deepset ai Haystack Pipeline Run For Evaluation
- Implementation:BerriAI Litellm Managed Files
- Implementation:LMCache LMCache SegmentTokenDatabase Process Tokens
- Implementation:Openai Openai python API Model Type
- Implementation:Hiyouga LLaMA Factory Data Collator
- Implementation:Langfuse Langfuse Traces Repository
- Implementation:Apache Dolphinscheduler DataSourceProcessorManager SPI Loading
- Implementation:AUTOMATIC1111 Stable diffusion webui Call Queue
- Implementation:Microsoft Onnxruntime OnnxSphinx
Heuristics
- Heuristic:Datajuicer Data juicer Batch Size Adaptation
- Heuristic:Mage ai Mage ai Batch Size Tuning
- Heuristic:Dotnet Machinelearning Sparsity Threshold Optimization
- Heuristic:MaterializeInc Materialize Pipeline Test Trimming Rules
- Heuristic:Roboflow Rf detr EMA Best Checkpoint Strategy
- Heuristic:Microsoft Agent framework Typed Agent Options Pattern
- Heuristic:Mlc ai Web llm Grammar Matcher Reuse
- Heuristic:Iamhankai Forest of Thought Input Length Overflow Recovery
- Heuristic:Axolotl ai cloud Axolotl Sample Packing Best Practices
- Heuristic:Sail sg LongSpec Triton Block Size Tuning
Environments
- Environment:DataExpert io Data engineer handbook Flink Kafka Docker Environment
- Environment:Eventual Inc Daft Ray Distributed Runner
- Environment:Vespa engine Vespa POSIX Mmap Log Control
- Environment:Mistralai Client python Azure Deployment Environment
- Environment:Puppeteer Puppeteer Cross Platform Browser Environment
- Environment:Marker Inc Korea AutoRAG VLLM Environment
- Environment:Apache Flink Node Build Environment
- Environment:Lm sys FastChat API Keys And Credentials
- Environment:Neuml Txtai Optional Extras Dependencies
- Environment:PacktPublishing LLM Engineers Handbook VLLM Evaluation Environment