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Software Engineering for Machine Learning

84 items from se-ml/awesome-seml ★1,373

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  1. Software Engineering for Machine Learning: A Case Study microsoft.com

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    site topic aitopic programming link ↗

  2. How do you manage your Machine Learning Experiments? medium.com

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  3. Nitpicking Machine Learning Technical Debt matthewmcateer.me

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  4. Continuous Delivery for Machine Learning martinfowler.com

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    site topic aitopic programming link ↗

  5. Machine Learning Logistics mapr.com

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  6. On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach link.springer.com

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  7. The Data Linter: Lightweight, Automated Sanity Checking for ML Data Sets learningsys.org

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  8. Kubeflow kubeflow.org

    A platform for data scientists who want to build and experiment with ML pipelines.

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  9. Operational Machine Learning kdnuggets.com

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  10. Pitfalls and Best Practices in Algorithm Configuration jair.org

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  11. ModelOps: Cloud-based lifecycle management for reliable and trusted AI hummer.io

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  12. Machine Learning Interviews github.com

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  13. Preparing and Architecting for Machine Learning gartner.com

    site topic ai link ↗

  14. FairLearn fairlearn.github.io

    A toolkit to assess and improve the fairness of machine learning models.

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  15. Versioning for end-to-end machine learning pipelines doi.org

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  16. Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing dl.acm.org

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  17. TFX: A tensorflow-based Production-Scale ML Platform dl.acm.org

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  18. Understanding Software-2.0 dl.acm.org

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  19. Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement dl.acm.org

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  20. Testing and Debugging in Machine Learning developers.google.com

    site topic aitopic math link ↗

  21. Principled Machine Learning: Practices and Tools for Efficient Collaboration dev.to

    site topic aitopic programming link ↗

  22. A Survey on Data Collection for Machine Learning A Big Data - AI Integration Perspective_2019 deepai.org

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  23. Managing Machine Learning Projects d1.awsstatic.com

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  24. Beyond Debiasing complexdiscovery.com

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  25. The ultimate guide to data labeling for ML cloudfactory.com

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  26. Machine learning: Moving from experiments to production blog.codecentric.de

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  27. An Architectural Risk Analysis Of Machine Learning Systems berryvilleiml.com

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  28. The curse of big data labeling and three ways to solve it aws.amazon.com

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  29. A Human-Centered Interpretability Framework Based on Weight of Evidence arxiv.org

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  30. Machine Learning Testing: Survey, Landscapes and Horizons arxiv.org

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    site topic aitopic programmingtopic sciencetopic mathtopic books & writing link ↗

  31. On human intellect and machine failures: Troubleshooting integrative machine learning systems arxiv.org

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  32. Underspecification Presents Challenges for Credibility in Modern Machine Learning arxiv.org

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  33. Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims arxiv.org

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  34. Preliminary Systematic Literature Review of Machine Learning System Development Process arxiv.org

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  35. Inherent trade-offs in the fair determination of risk scores arxiv.org

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  36. How to organize data labelling for ML altexsoft.com

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  37. Best Practices in Machine Learning Infrastructure algorithmia.com

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  38. Aim aimstack.io

    Aim is an open source experiment tracking tool.

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  39. Fairness Indicators: Scalable Infrastructure for Fair ML Systems ai.googleblog.com

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  40. Responsible AI practices ai.google

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  41. Pitfalls of supervised feature selection academic.oup.com

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