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.

1. AI at Microsoft
HEC
Sept 24th 2018

2. Speaker
Alex Danvy
Microsoft
@danvy

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)

9. © Microsoft Corporation

10. © Microsoft Corporation
Research

13. © Microsoft Corporation
Products

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

16. AI Powered Spreadsheets

17. Seamless typing with the keyboard
that learns from you.

18. Skype Translator

19. Seeing AI

21. © Microsoft Corporation
Infrastructure

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

23. Azure
AI Supercomputer

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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.

31. © Microsoft Corporation
Demo

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

46. © Microsoft Corporation
Demo

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