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[第45回 Machine Learning 15minutes! Broadcast] Azure AI - Build 2020 Updates

[第45回 Machine Learning 15minutes! Broadcast] Azure AI - Build 2020 Updates

[第45回 Machine Learning 15minutes! Broadcast] Azure AI - Build 2020 Updates

https://satonaoki.wordpress.com/2020/06/02/ml15min-azure-ai-build-2020-updates/

0f993881fa8709dbe986bcade6c1fbad?s=128

SATO Naoki (Neo)

May 30, 2020
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  1. Azure AI – Build 2020 Updates SATO Naoki (Neo) Senior

    Software Engineer Microsoft
  2. None
  3. Fueled by breakthrough research 2016 Object recognition human parity 2017

    Speech recognition human parity 2018 Reading comprehension human parity 2018 Machine translation human parity 2018 Speech synthesis near-human parity 2019 General Language Understanding human parity 2020 Document summary at human parity
  4. 1.8M Hours of meetings transcribed in real-time 1B PowerPoint Designer

    slides used Tested at scale in Microsoft solutions 80M Personalized experiences delivered daily Machine translation human parity Object detection human parity Switchboard Switchboard cellular Meeting speech IBM Switchboard Broadcast speech Speech recognition human parity Conversational Q&A human parity First FPGA deployed in a datacenter
  5. Power Platform Power BI Power Apps Power Automate Power Virtual

    Agents Azure 58 Regions 90+ Compliance Offerings $1B Security investment per year 95% Fortune 500 use Azure Azure AI ML platform Customizable models Vision, Speech, Language, Decision Scenario-specific services Cognitive Services Azure Machine Learning Data Platform App Dev Platform & tools Compute Cognitive Search Bot Service Form Recognizer Video Indexer
  6. Cloud CPU, GPU, FPGA Datasets Profiling, Drift, Labeling Inferencing Batch,

    Realtime MLOps Reproducible, Automatable, GitHub, CLI, REST Experience SDK, Notebooks, Drag-n-drop, Wizard Edge CPU, GPU, NPU IoT Edge Security, Mgmt, Deployment Compute Jobs, Clusters, Instances Model Registry Models, Images Training Experiments, Runs
  7. Responsible AI

  8. Practices Principles Tools AETHER committee The Partnership on AI Guidelines

    for Human-AI Design Homomorphic Encryption Interpret ML Differential Privacy Data Drift Secure MPC Guidelines for Conversational AI Fairness Reliability Privacy Inclusivity Accountability Transparency
  9. Why responsible AI? Report

  10. Understand Interpretability Fairness Azure Machine Learning Responsible ML Control Audit

    trail Datasheets Protect Differential privacy Confidential machine learning
  11. Understand Interpretability Fairness Azure Machine Learning Responsible ML Control Audit

    trail Datasheets Protect Differential privacy Confidential machine learning
  12. Loan Application Decisions Azure Machine Learning How does it decide

    who to accept or reject? Is my model fair? Create a model for loan application acceptance
  13. Loan Application Decisions Azure Machine Learning How does it decide

    who to accept or reject? Is my model fair? Create a model for loan application acceptance
  14. Fairness in AI There are many ways that an AI

    system can behave unfairly A voice recognition system might fail to work as well for women as it does for men A model for screening loan or job application might be much better at picking good candidates among white men than among other groups Avoiding negative outcomes of AI systems for different groups of people
  15. Assessing unfairness in your model https://github.com/fairlearn/fairlearn Fairness assessment: Usecommonfairness metrics

    andaninteractive dashboardto assess which groups of peoplemay benegatively impacted Model formats: Python models using scikit predict convention, Scikit, Tensorflow, Pytorch, Keras Metrics: 15+ common group fairness metrics Model types: Classification, Regression Fairness mitigation: Use state-of-the-art algorithms to mitigate unfairness in your classificationandregressionmodels
  16. Fairness Assessment Input Selections Sensitive attribute Performance metric Assessment Results

    Disparity in performance Disparity in predictions
  17. Fairness Mitigation Fairness Criteria Demographic Parity Equalized Odds Mitigation Algorithm

    Postprocessing Method Reduction Method
  18. Loan Application Decisions Azure Machine Learning How does it decide

    who to accept or reject? Is my model fair? Create a model for loan application acceptance
  19. Understand and debug your model Interpret Glassbox and blackbox interpretability

    methods for tabular data Interpret- community Additional interpretability techniques for tabular data Interpret-text Interpretability methods for text data DiCE Diverse Counterfactual Explanations Blackbox models: Model formats: Python models using scickit predict convention, Scikit, Tensorflow, Pytorch, Keras Explainers: SHAP, LIME, Global Surrogate, Feature Permutation Glassbox Models: Model types: Linear Models, Decision Trees, Decision Rules, Explainable Boosting Machines AzurML-interpret AzureML SDK wrapper for Interpret and Interpret-community https://github.com/interpretml
  20. Interpretability approaches Glassbox models Blackbox explanations

  21. Fever? Internal bleeding? Stay home Stay home Go to hospital

    Models designed to be interpretable. Lossless explainability. Glassbox models Decision trees Rule lists Linear models Explainable Boosting Machines
  22. Explain any ML system. Approximate explainability. Blackbox explanations Model Explanation

    Perturb inputs Analyze Shap Lime Partial dependence Sensitivity analysis
  23. Responsible ML resources Microsoft Responsible AI Resource Center https://aka.ms/RAIresources Azure

    Machine Learning https://azure.microsoft.com/en-us/services/machine- learning/ https://docs.microsoft.com/en-us/azure/machine- learning/concept-responsible-ml Responsible Innovation Toolkit https://docs.microsoft.com/azure/architecture/guide/resp onsible-innovation FairLearn https://github.com/fairlearn https://aka.ms//FairLearnWhitepaper https://docs.microsoft.com/azure/machine- learning/concept-fairness-ml InterpretML https://github.com/interpretml https://aka.ms//InterpretMLWhitepaper https://docs.microsoft.com/azure/machine-learning/how- to-machine-learning-interpretability
  24. Understand Interpretability Fairness Azure Machine Learning Responsible ML Control Audit

    trail Datasheets Protect Differential privacy Confidential machine learning
  25. Differential Privacy for Machine Learning and Analytics https://github.com/opendifferentialprivacy Native Runtime

    C,C++, Python, R Validator Automatically stress test DP algorithms Data Source Connectivity Data Lakes, SQL Server, Postgres, Apache Spark, Apache Presto and CSV files Privacy Budget Control queries by users
  26. WhiteNoise Privacy Module Report Budget Store BUDGET User Private Dataset

    Submits a query Receives a differentially private report Mechanism adds noise Private data Dataset checks budget and access credentials Checks budget and private compute Credentials to access the data https://github.com/opendifferentialprivacy
  27. Confidential ML Technologies Privacy Barrier Privacy Barrier Confidential from Data

    Scientist Encrypted using Hardware
  28. Homomorphic Encryption Decrypt(Encrypt(A) + Encrypt(B)) = A + B Decrypt(Encrypt(A)

    * Encrypt(B)) = A * B Privacy Barrier Homomorphic encryption allows certain computations to be done on encrypted data, without requiring any decryption in the process: Different from classical encryption like AES or RSA:
  29. Microsoft SEAL Open-source homomorphic encryption library Developed actively since 2015

    Recently released v3.5 Available at GitHub.com/Microsoft/SEAL Supports Windows, Linux, macOS, Android, FreeBSD Written in C++; includes .NET Standard wrappers for public API From open source community: PyHeal (Python wrappers from Accenture) node-seal (JavaScript wrappers) nGraph HE Transformer (from Intel)
  30. Private Prediction on Encrypted Data Through a trained machine learning

    model, private prediction enables inferencing on encrypted data without revealing the content of the data to anyone. Microsoft SEAL can be deployed in a variety of applications to protect users personal and private data Privacy Barrier [cryptographic] Medical prediction
  31. Responsible ML Resources  Microsoft Responsible AI Resource Center 

    https://aka.ms/RAIresources  Azure Machine Learning  https://azure.microsoft.com/en- us/services/machine-learning/  https://docs.microsoft.com/en- us/azure/machine-learning/concept- responsible-ml  OpenDP  http://opendp.io/  https://twitter.com/opendp_io  WhiteNoise  https://github.com/opendifferentialprivacy  https://docs.microsoft.com/azure/machine- learning/concept-differential-privacy  https://docs.microsoft.com/azure/machine- learning/how-to-differential-privacy  https://aka.ms/WhiteNoiseWhitePaper  SEAL  https://github.com/Microsoft/SEAL  https://docs.microsoft.com/azure/machine- learning/how-to-homomorphic-encryption- seal  https://aka.ms/SEALonAML
  32. AI at Scale

  33. Next-gen AI capabilities To accelerate the change we needed, we

    took advantage of three trends Transfer learning Large pre-trained self supervised networks Culture shift
  34. Microsoft Turing NLG 5 b 7.5 b 10 b 12.5

    b 15 b 17.5 b Spring ‘18 Summer ‘18 Autumn ‘18 Winter ‘19 Spring ‘19 Summer ‘19 Autumn ‘19 Winter ‘20 2.5 b ELMo 94m GPT 110m BERT - large 340 m Transformer ELMo 465m GPT-2 1.5b MT-DNN 330m XLNET 340m XLM 665m Grover-Mega 1.5b RoBERTa 355m DistilBERT 66m MegatronLM 8.3b T-NLG 17b Number of parameters
  35. Custom capabilities through reuse Turing NLP

  36. Microsoft Bing Intelligent Answers which summarize the web

  37. Microsoft Word Smart Find Search inside your document as easily

    as is to search the web
  38. Microsoft SharePoint Inside Look Find that document you’re looking for

    by browsing automatic document summaries
  39. Microsoft Outlook Suggested Replies Save time by responding with AI

    assisted suggestions
  40. AI at Scale for everyone

  41. Collaboration with OpenAI Hosted in Azure 285,000 CPU cores, 10,000

    GPUs, 400 Gbps for each GPU server Top 5 in Top 500 SCs https://blogs.microsoft.co m/ai/openai-azure- supercomputer/
  42. Learn more: • AI at Scale introduction: aka.ms/AIatScale • AI

    at Scale Deep Dive: aka.ms/AIS-DeepDive • DeepSpeed library: github.com/microsoft/DeepSpeed • ONNX Runtime: aka.ms/onnxruntime • Try T-NLG in the Companion App: aka.ms/Build-AIS
  43. Rethinking the AI Stack NVidia GPUs Intel FPGAs NVLink Infiniband

    DeepSpeed allowing for training models 15x bigger, 10x faster on the same infrastructure ONNX Runtime Central AI group coordinating bringing the best of research into products All available on Azure and GitHub for everyone!
  44. © Copyright Microsoft Corporation. All rights reserved.