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Software Engineering for Machine Learning

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

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  1. Engineering Best Practices for Machine Learning se-ml.github.io
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  2. Fairness On The Ground: Applying Algorithmic FairnessApproaches To Production Systems scontent-amt2-1.xx.fbcdn.net
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  3. AI Engineering: 11 Foundational Practices resources.sei.cmu.edu
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  4. The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction research.google
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  5. Scaling Machine Learning as a Service proceedings.mlr.press
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  6. Best Practices for Machine Learning Applications pdfs.semanticscholar.org
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  7. Hidden Technical Debt in Machine Learning Systems papers.nips.cc
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  8. Facets Overview / Facets Dive pair-code.github.io
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    Robust visualizations to aid in understanding machine learning datasets.

  9. Building Continuous Integration Services for Machine Learning pages.cs.wisc.edu
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  10. OpenML openml.org
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    An inclusive movement to build an open, organized, online ecosystem for machine learning.

  11. 10 Best Practices for Deep Learning nanonets.com
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  12. Data Validation for Machine Learning mlsys.org
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  13. What Went Wrong and Why? Diagnosing Situated Interaction Failures in the Wild microsoft.com
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  14. Software Engineering for Machine Learning: A Case Study microsoft.com
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  15. How do you manage your Machine Learning Experiments? medium.com
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  16. Nitpicking Machine Learning Technical Debt matthewmcateer.me
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  17. Continuous Delivery for Machine Learning martinfowler.com
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  18. Machine Learning Logistics mapr.com
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  19. On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach link.springer.com
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  20. The Data Linter: Lightweight, Automated Sanity Checking for ML Data Sets learningsys.org
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  21. Kubeflow kubeflow.org
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    A platform for data scientists who want to build and experiment with ML pipelines.

  22. Operational Machine Learning kdnuggets.com
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  23. Pitfalls and Best Practices in Algorithm Configuration jair.org
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  24. ModelOps: Cloud-based lifecycle management for reliable and trusted AI hummer.io
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  25. Machine Learning Interviews github.com
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  26. Preparing and Architecting for Machine Learning gartner.com
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  27. FairLearn fairlearn.github.io
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    A toolkit to assess and improve the fairness of machine learning models.

  28. Versioning for end-to-end machine learning pipelines doi.org
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  29. Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing dl.acm.org
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  30. TFX: A tensorflow-based Production-Scale ML Platform dl.acm.org
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  31. Understanding Software-2.0 dl.acm.org
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  32. Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement dl.acm.org
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  33. Testing and Debugging in Machine Learning developers.google.com
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  34. Principled Machine Learning: Practices and Tools for Efficient Collaboration dev.to
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  35. A Survey on Data Collection for Machine Learning A Big Data - AI Integration Perspective_2019 deepai.org
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  36. Managing Machine Learning Projects d1.awsstatic.com
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  37. Beyond Debiasing complexdiscovery.com
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  38. The ultimate guide to data labeling for ML cloudfactory.com
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  39. Machine learning: Moving from experiments to production blog.codecentric.de
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  40. An Architectural Risk Analysis Of Machine Learning Systems berryvilleiml.com
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  41. The curse of big data labeling and three ways to solve it aws.amazon.com
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  42. A Human-Centered Interpretability Framework Based on Weight of Evidence arxiv.org
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  43. Machine Learning Testing: Survey, Landscapes and Horizons arxiv.org
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  44. On human intellect and machine failures: Troubleshooting integrative machine learning systems arxiv.org
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  45. Underspecification Presents Challenges for Credibility in Modern Machine Learning arxiv.org
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  46. Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims arxiv.org
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  47. Preliminary Systematic Literature Review of Machine Learning System Development Process arxiv.org
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  48. Inherent trade-offs in the fair determination of risk scores arxiv.org
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  49. How to organize data labelling for ML altexsoft.com
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  50. Best Practices in Machine Learning Infrastructure algorithmia.com
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