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[Machine Learning 15minutes! Broadcast #67] Azure AI - Build 2022 Updates and more...

[Machine Learning 15minutes! Broadcast #67] Azure AI - Build 2022 Updates and more...

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SATO Naoki (Neo)

June 25, 2022
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  1. Azure AI – Build 2022 Updates and more... SATO Naoki

    (Neo) Senior Software Engineer, Microsoft
  2. None
  3. How customers adopt AI  Knowledge Mining 60 % 

    Reduction in cost of compliance  Conversational AI 30 %  Of all e-commerce will be through voice by 2030  Document Process Automation 46 %  Cost savings with process automation in IT enterprises  Machine Translation 78 %  Of language services utilize Machine Translation  Speech Transcription & Analytics 64%  Of online education experts use automated transcription Build, deploy and manage custom ML models Solve common business scenarios Knowledge Mining Conversational AI Business Process Automation Machine Translation Speech Transcription & Analytics
  4. None
  5. ML platform Azure Machine Learning Azure AI Vision Speech Language

    Decision OpenAI Service Customizable, high quality AI models Cognitive Services Bot Service Cognitive Search Form Recognizer Video Indexer Metrics Advisor Immersive Reader Scenario specific AI services Applied AI Services
  6. Preview Preview Preview Auto-tuning capability Preview Gated Preview Gated Preview

    Gated Preview
  7. Preview Document & Conversation Summarization Preview Custom Named Entity Recognition

    Generally Available Custom Text Classification Generally Available Conversational Language Understanding Generally Available Document Translation Generally Available
  8. Public Preview

  9. GitHub Copilot https://github.com/features/copilot/

  10. ML platform Azure Machine Learning Azure AI Vision Speech Language

    Decision OpenAI Service Customizable, high quality AI models Cognitive Services Bot Service Cognitive Search Form Recognizer Video Indexer Metrics Advisor Immersive Reader Scenario specific AI services Applied AI Services
  11. Data Preparation Model Building Model Training Model Management & Monitoring

    Model Deployment Responsible AI MLOps
  12. Structured Data Unstructured Data Analytics Business Apps Prepare Data Build

    & Train Manage & Monitor Deploy across clouds and on-premises Serverless Compute Managed Kubernetes Azure Edge & Hybrid Azure Arc-enabled Kubernetes Edge/IoT Devices Azure ML
  13. MLOps

  14. Preview Generally Available Preview Generally Available  Support for natural

    language processing and image tasks  Generation of model training codes  Enhancements for product integration and MLOps
  15. Azure ML Azure ML Your editor of choice

  16. Pipeline Metadata name, display_name, version, type, etc. Interface input/output specifications

    (name, type, description, default value, etc). Command, Code & Environment command, code and environment required to run the component SDK CLI UI
  17. Fairness Inclusiveness Privacy & Security Transparency Reliability & Safety Accountability

    Microsoft’s AI Principles
  18. Responsible AI investments and safeguards for facial recognition https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/

  19. None
  20. Azure Machine Learning Responsible AI Dashboard & Responsible AI Scorecard

    Managed endpoints Python SDK v2 Arc support Command Line Interface v2 Custom reusable components (in pipelines) Automated ML new features Support for natural language processing and image tasks Generation of model training codes Enhancements for product integration and MLOps Integration with RStudio Workbench
  21. None