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AI

Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.

  1. How We Built Our Multi-Agent Research System anthropic.com

    Production lessons on orchestrator-worker agents, parallel search, evaluation, and operational reliability.

  2. Demystifying Evals for AI Agents anthropic.com

    A practical method for building task suites, graders, transcripts, and evaluation harnesses.

  3. Artificial Intelligence: A Modern Approach aima.cs.berkeley.edu

    The broad reference for classical AI, including search, reasoning, planning, learning, and robotics.

  4. AI Engineer aiengineer.co

    A paid program for agentic coding and building, testing, and shipping production AI systems.

  5. Pydantic AI ai.pydantic.dev

    Typed agent development built around Pydantic.

  6. Sibyl users.soe.ucsc.edu

    System for Large Scale Machine Learning at Google.

  7. Azure ML Studio studio.azureml.net

    Cloud-based AzureML, R, Python Machine Learning platform

  8. Applied Network Science appliednetsci.springeropen.com

  9. Spark MLlib spark.apache.org

    a Spark implementation of some common machine learning (ML) functionality.

  10. PredictionIO predictionio.incubator.apache.org

    machine learning server built on Hadoop, Mahout and Cascading.

  11. MOA moa.cms.waikato.ac.nz

    MOA performs big data stream mining in real time, and large scale machine learning.

  12. MLbase mlbase.org

    distributed machine learning libraries for the BDAS stack.

  13. Graph-Powered Machine Learning manning.com

    Alessandro Negro. Combine graph theory and models to improve machine learning projects

  14. ENCOG heatonresearch.com

    machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data.

  15. Torch github.com

    A deep learning library with a Lua API, supported by NYU and Facebook.

  16. Theano github.com

    A Python-focused machine learning library supported by the University of Montreal.

  17. RL4J github.com

    Reinforcement learning for Java and Scala. Includes Deep-Q learning and A3C algorithms, and integrates with Open AI's Gym. Runs in the Deeplearning4j ecosystem.

  18. DataVec github.com

    A vectorization and data preprocessing library for deep learning in Java and Scala. Part of the Deeplearning4j ecosystem.

  19. Deeplearning4j github.com

    Fast, open deep learning for the JVM (Java, Scala, Clojure). A neural network configuration layer powered by a C++ library. Uses Spark and Hadoop to train nets on multiple GPUs and CPUs.

  20. etcML etcml.com

    text classification with machine learning.

  21. LinkedIn Galene engineering.linkedin.com

    search architecture at LinkedIn.

  22. Apache SystemML systemml.apache.org

    Declarative machine learning framework on top of Spark.

  23. Advanced Analytics with Spark shop.oreilly.com

    Useful collection of Spark processing patterns. Accompanying GitHub repository: sryza/aas.

  24. PredictionIO prediction.io

    Machine Learning server for developers and data scientists to build and deploy predictive applications in a fraction of the time.

  25. GraphLab Create dato.com

    A machine learning platform in Python with a broad collection of ML toolkits, data engineering, and deployment tools.

  26. MLflow mlflow.org

    Machine learning orchestration platform.

  27. ModelDB mitdbg.github.io

    A system to manage machine learning models for spark.ml and scikit-learn .

  28. Datapallas datapallas.com

    BI and data platform with AI exploration, dashboards, and pixel-perfect report generation; formerly ReportBurster.

  29. KeystoneML keystone-ml.org

    Type safe machine learning pipelines with RDDs.

  30. Comet comet.com

    Comet provides an end-to-end model evaluation platform for AI developers, with best in class LLM evaluations, experiment tracking, and production monitoring.

  31. Concurrent Pattern cascading.org

    machine learning library for Cascading.

  32. 2013 amplab.cs.berkeley.edu

    AMPLab - Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices.

  33. 2013 amplab.cs.berkeley.edu

    AMPLab - MLbase: A Distributed Machine-learning System.

  34. Data Science and Engineering with Apache Spark (edX XSeries) edx.org

    Series of five courses (Introduction to Apache Spark, Distributed Machine Learning with Apache Spark, Big Data Analysis with Apache Spark, Advanced Apache Spark for Data Science and Data Engineering, Advanced Distributed Machine Learning with Apache Spark) covering different…

  35. Anthony Tellez anthonygtellez.github.io

    Security and Machine Learning items.

  36. Zingg zingg.ai

    Open source Master Data Management platform using machine learning for entity resolution at scale. Native to Databricks, Microsoft Fabric, Snowflake, AWS, and GCP. Golden records are maintained through a persistent Zingg ID across all systems and sources.

  37. Xquik xquik.com

    Real-time X (Twitter) data extraction platform with REST API (76 endpoints), 20 bulk extraction tools, account monitoring, HMAC-signed webhooks, and MCP server for AI agent integration.

  38. Spark MLlib spark.apache.org

    Spark's scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives.

  39. Rivestack rivestack.io

    Managed PostgreSQL with pgvector for AI workloads. HNSW indexing, sub-4ms latency, and built-in SQL editor with automatic embedding generation.

  40. Practical AI practicalai.fm

    Making AI practical, productive, and accessible to everyone.

  41. Learn AI Data Engineering in a Month of Lunches manning.com

    A fast, friendly guide to integrating large language models into your data workflows.

  42. LunaPad lunapad.dev

    Open-source analytics notebook for reusable SQL workflows, interactive reports, and AI-assisted data exploration.

  43. Latent Space latent.space

    Technical deep dives on AI engineering, from model training to deployment.

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