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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:Allenai Open instruct Tulu3 Full Post Training
- Workflow:Bigscience workshop Petals Prompt Tuning Classification
- Workflow:DataTalksClub Data engineering zoomcamp dbt Analytics Transformation
- Workflow:Ollama Ollama Safetensors To GGUF Conversion
- Workflow:Intel Ipex llm vLLM Serving
- Workflow:Princeton nlp SimPO SimPO Training
- Workflow:Truera Trulens LangGraph Agent Evaluation
- Workflow:Trailofbits Fickling Pickle Decompilation and Tracing
- Workflow:Microsoft Autogen Graph Based Agent Orchestration
- Workflow:PrefectHQ Prefect API Sourced ETL
Principles
- Principle:Openai Openai python Input Token Counting
- Principle:ClickHouse ClickHouse Data Lake Testing
- Principle:Spotify Luigi Distributed Processing
- Principle:Ggml org Ggml ZDNN Accelerated Computation
- Principle:Confident ai Deepeval Offline Trace Evaluation
- Principle:Langchain ai Langchain Public API Surface
- Principle:Apache Dolphinscheduler RPC Retry And Error Recovery
- Principle:Facebookresearch Audiocraft Loudness Ratio Loss
- Principle:Tensorflow Serving JSON Request Formatting
- Principle:Openai CLIP Zero Shot Classifier Construction
Implementations
- Implementation:Langchain ai Langgraph Pregel Invoke For Agents
- Implementation:Tensorflow Serving Serving Session
- Implementation:Ggml org Llama cpp Llama Adapter LoRA Init
- Implementation:Huggingface Datasets TestCommand
- Implementation:Facebookresearch Audiocraft MusicDataset init
- Implementation:Microsoft Onnxruntime SaveCheckpoint
- Implementation:Pola rs Polars LazyFrame Explain Show Graph
- Implementation:Recommenders team Recommenders SASRec Sampler
- Implementation:Arize ai Phoenix OTLP Server Receiver
- Implementation:Recommenders team Recommenders Surprise Utils
Heuristics
- Heuristic:Open compass VLMEvalKit SKIP ERR For Graceful Inference
- Heuristic:DevExpress Testcafe CDP Performance Monitoring
- Heuristic:Snorkel team Snorkel Minimum Three LFs
- Heuristic:NVIDIA DALI Distributed Sharding Strategy
- Heuristic:CarperAI Trlx PEFT LoRA Integration
- Heuristic:DataExpert io Data engineer handbook Flink Checkpointing Interval Tuning
- Heuristic:Romsto Speculative Decoding Shared Tokenizer Requirement
- Heuristic:Treeverse LakeFS Action Cache Wait Tip
- Heuristic:Recommenders team Recommenders SAR Cold Start Items
- Heuristic:Norrrrrrr lyn WAInjectBench Union Ensemble Maximize Recall
Environments
- Environment:Duckdb Duckdb Release Publishing Env
- Environment:Vllm project Vllm ROCm
- Environment:Vllm project Vllm NVIDIA CUDA
- Environment:Langgenius Dify Credentials And Env Vars
- Environment:Apache Dolphinscheduler Java Runtime
- Environment:BerriAI Litellm PostgreSQL Database
- Environment:Anthropics Anthropic sdk python Azure Foundry Environment
- Environment:Run llama Llama index Python LlamaIndex Core
- Environment:Langgenius Dify Docker Deployment Environment
- Environment:Openai Openai agents python Python 3 9 Runtime