AI at Microsoft for HEC
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The document discusses Microsoft's approach to artificial intelligence and machine learning. It aims to make AI available to everyone through products like Cortana, Office 365, Dynamics 365, and Azure services. It describes Microsoft's research in areas like cloud computing, big data, and powerful algorithms. The document also provides examples of Microsoft's AI portfolio including agent applications, services, infrastructure, and tools to build custom solutions. It demonstrates tools like Azure Machine Learning Studio, the Data Science Virtual Machine, and Azure Cognitive Services.
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AI at Microsoft for HEC
- 1. AI at Microsoft HEC Sept 24th 2018
- 3. Microsoft’s aim: Make AI available to everyone We are pursuing AI to empower every person and every institution ... so that they can go on to solve the most pressing problems of our society and our economy. – Satya Nadella, CEO, Microsoft
- 4. © Microsoft Corporation Breakthroughs Enabling Intelligence Cloud Computing Big Data Powerful Algorithms
- 6. © Microsoft Corporation What is Machine Learning?
- 7. What is Machine Learning? (I) Age Income Education Gender Housing 61 $65,000 Moderate F Own 42 $72,000 High F Rent 18 $25,000 Moderate M Other 22 $36,000 Low M Rent 31 $52,000 High M ?
- 8. What is a Machine Learning? (II)
- 14. Agent Applications Services Infrastructure Microsoft AI Portfolio Cortana Office 365 Dynamics 365 Bot Framework Cognitive Services Cortana Intelligence Cognitive Toolkit Azure Machine Learning Azure N Series FPGA Platform Approach For every person and every organization
- 15. Intelligence built in to Office 365 Cortana in Outlook – Your Digital PA Translator / Dictator Outlook Bots in Teams Focused inbox in Outlook Translator Word, PPT, OutlookFace Recognition in Stream
- 17. Seamless typing with the keyboard that learns from you.
- 18. Skype Translator
- 19. Seeing AI
- 22. Apps + insights Social LOB Graph IoT Image CRM INGEST STORE PREP & TRAIN MODEL & SERVE Data orchestration and monitoring Data lake and storage Hadoop/Spark/SQL and ML . IoT Azure Machine Learning T H E A I D E V E L O P M E N T L I F E C Y C L E
- 25. © Microsoft Corporation Tools to build your own solution
- 26. © Microsoft Corporation Azure Cognitive Services
- 27. Microsoft Services | Digital Advisors Zoom on cognitive services : build applications that understand people • Faces, images, emotion recognition and video intelligence • Spoken language processing, speaker recognition, custom speech recognition • Natural language processing, sentiment and topics analysis, spelling errors • Complex tasks processing, knowledge exploration, intelligent recommendations • Bing engine capabilities for Web, Autosuggest, Image, Video and News “on the shelf” Intelligence Cognitive Services
- 28. Microsoft Services | Digital Advisors Zoom on cognitive services : Build applications that understand people “on the shelf” Intelligence Cognitive Services
- 29. Machine learning & Intelligence Perceptuelle Analyse de Texte :Analyse d’image & D’émotions Moteur de recommandation & plus encore:
- 30. Bot Framework Your bots – wherever your users are talking. Build and connect intelligent bots to interact with your users naturally wherever they are, from text/SMS to Skype, Slack, Messenger, Office 365 mail and other popular services.
- 32. © Microsoft Corporation Azure Machine Learning Studio
- 33. Azure Machine Learning Studio Platform for emerging data scientists to graphically build and deploy experiments • Rapid experiment composition • > 100 easily configured modules for data prep, training, evaluation • Extensibility through R & Python • Serverless training and deployment Some numbers: • 100’s of thousands of deployed models serving billions of requests
- 35. © Microsoft Corporation Azure Machine Learning Workbench
- 36. Windows and Mac based companion for AI development Full environment set up (Python, Jupyter, etc) Embedded notebooks Run History and Comparison experience New data wrangling tools What Is It?
- 37. AI Powered Data Wrangling Rapidly sample, understand, and prep data Leverage PROSE and more for intelligent, data prep by example Extend/customize transforms and featurization through Python Generate Python and Pyspark for execution at scale
- 38. Experiment Manage job for local and cloud experiments Find support for Spark + Python + R (roadmap) Execute jobs locally, on remote VMs (scale up), on Spark clusters (scale out), or SQL on-premises Create with Git-backed experimentation tracking of code, config, parameters, and data Discover and compare with detailed historical metadata
- 39. © Microsoft Corporation Data Science VM
- 40. What is the Data Science VM? https://aka.ms/dsvm/overview
- 41. © Microsoft Corporation Azure ML Experimentation
- 42. Local machine Scale up to DSVM Scale out with Spark on HDInsight Azure Batch AI (Coming Soon) ML Server Experiment Everywhere A ZURE ML EXPERIMENTATION Command line tools IDEs Notebooks in Workbench VS Code Tools for AI
- 43. Manage project dependencies Manage training jobs locally, scaled-up or scaled-out Git based checkpointing and version control Service side capture of run metrics, output logs and models Use your favorite IDE, and any framework Experimentation service U S E T H E M O S T P O P U L A R I N N O V A T I O N S U S E A N Y T O O L U S E A N Y F R A M E W O R K O R L I B R A R Y
- 44. DOCKER Single node deployment (cloud/on-prem) Azure Container Service Azure IoT Edge Microsoft ML Server Spark clusters SQL Server Deploy Everywhere A ZURE ML MODEL MANAGEMENT
- 45. • Deployment and management of models as HTTP services • Container-based hosting of real time and batch processing • Management and monitoring through Azure Application Insights • First class support for SparkML, Python, Cognitive Toolkit, TF, R, extensible to support others (Caffe, MXnet) • Service authoring in Python
- 49. © Microsoft Corporation The tool you need
- 50. Machine Learning & AI Portfolio When to use what? What engine(s) do you want to use? Deployment target Which experience do you want? Build your own or consume pre- trained models? Microsoft ML & AI products Build your own Azure Machine Learning Code first (On-prem) ML Server On- prem Hadoop SQL Server (cloud) AML services (Preview) SQL Server Spark Hadoop Azure Batch DSVM Azure Container Service Visual tooling (cloud) AML Studio Consume Cognitive services, bots
- 51. © Copyright Microsoft Corporation. All rights reserved. Alex Danvy Microsoft @danvy