The document outlines the capabilities of Azure in building, training, and deploying intelligent applications using AI. It covers various Azure services, including cognitive services, Azure ML, and Azure bot services, highlighting how developers can create conversational and data-driven applications. Additionally, it emphasizes Azure's flexibility, scalability, and integration with various AI frameworks and tools.

6. Helpicto Architecture
DATA PREP BUILD, TRAIN & DEPLOY INTELLIGENT APPS
Stored on
Azure Blob and SQL DB
Xamarin
Helpicto Images
Cognitive Services,
Azure Functions

7. Bringing the best of AI to Azure and the best of Azure to AI
Pre-Built AI
Azure Cognitive Services
Conversational AI
Azure Bot Service
Custom AI
Azure Machine Learning

8. How Developers Use AI in Azure
Build Modern Applications with AI
Understand & interpret meaning of data
including text, voice, images
Build Conversational Applications
Engage with users in natural ways
Optimize Business Processes with AI
Reason, learn & form conclusions from data
Azure
Cognitive Services
Azure
Bot Services
Azure
Machine Learning

9. Powerful prebuilt AI models exposed as API services
Simple REST APIs with .NET, Java, Python, Node SDKs
Easily customize for highest accuracy
Train in the cloud and deploy anywhere
Vision
Speech
Language
Conversation
Bing Search
Knowledge

11. 1. Object Detection & Recognition for thousands of objects
2. Video Indexer (Preview)
3. Speech Recognition with customization
4. Speech Synthesis with customizable voice
5. Speech to Speech Translation
6. Text analytics with entity detection
7. Language Understanding (LUIS) with new integrated offer
8. QnA Maker is Generally Available
9. Bing Visual Search with smart identification
10.Bing Search SDK is Generally Available
Vision
Speech
Language
Conversation
Bing Search
Knowledge

12. Vision
Speech
Language
Conversation
Bing Search
Knowledge
Azure Search with
Cognitive Services
Built-in enrichment of content using AI models
Enable immersive search experiences over any data

13. Brian Smith
Principal Engineering Manager
@BrianSmithMSFT

14. November 22, 1963
John F. Kennedy (JFK)

15. HOW DO WE DO THIS?
Ingest Enrich Explore

16. Ingest Enrich Explore
Data Cognitive Skills Azure Search
AN AI–FIRST APPROACH TO CONTENT UNDERSTANDING
Cognitive Search

18. JFK FILES
COGNITIVE SEARCH
ARCHITECTURE
Explore
Azure
Storage
Azure Functions
-Cryptonyms
-Redactions
Cognitive Skills
-OCR + Handwriting
-Computer Vision
-Entities
Azure ML
Search Index
Azure Search
The JFK Files Architecture
Content
Extraction

19. ANALYZING
LEGAL CONTRACTS
UNDERSTANDING
ENGINEERING PLANS
EXTRACTING
FORM INFORMATION
Bring AI to your data

20. AZURE SEARCH PORTAL JFK FILES ON GITHUB
https://aka.ms/jfk-fileshttps://portal.azure.com
Get started today

21. Azure Search with built-in AI
COGNITIVE SEARCH BREAKOUT SESSION
Learn more at session 3201
Learn more at //build

23. Conversational AI
American Express Briefing – Jan 17, 2018
Chris Mullins
Group Program Manager, Azure Bot Service

24. 190,000 total bots registered
33000 active bots in service
>3200 bots created per week

25. What is a Bot?
{ Your Code }
Intelligence Cognitive Services
Tools Bot Framework
HTTP
REST Endpoint
Services Bot Service
Cortana OthersFacebookSkype
Conversational and
Business logic
Development
tools

26. Knowledge
Cognitive Search Custom Speech
Understanding
Custom Voice
Personality
Azure Bot Service
Conversational AI

27. Noelle LaCharite
Principal Program Manager
@NoelleLaCharite
Wilson Lee
Senior Software Engineer
@SimplyWilson

38. BRK 3235:
BRK 3208:
THR 3118:

40. Building your own AI models for Transforming Data into Intelligence
Prepare Data Build & Train Deploy

41. Quickly launch and scale Spark on demand
Rich interactive workspace and notebooks
Seamless integration with all Azure data services
Azure Databricks
Step 1: Prepare Data
Apache® Spark™ based analytics
platform optimized for Azure

42. Broad frameworks and tools support:
 TensorFlow, Cognitive Toolkit, Caffe2, Keras, MxNET, PyTorch
Scale training from 1  100,000’s of servers
Azure ML Python SDK
Azure ML packages:
 Computer Vision, Text, Forecasting
Azure Machine Learning
Step 2: Build and Train

43. Docker
Containers
Azure Kubernetes Service (AKS)
Azure Batch
Azure IoT EdgeAzure
Machine Learning
Step 3: Deploy
Any other container host…

44. Integrated with Azure Machine Learning
Create new deep learning projects easily
Monitor model training progress & GPU utilization
Visualize your model performance with integrated
open tools like TensorBoard
Get started quickly with the Samples Gallery

45. The Machine Learning framework made by and for .NET developers
Proven & Extensible
Open Source
Supported on Windows, Linux, and macOS
Developer Focused
Join at github.com/dotnet/machinelearning
ML.NET Preview
Cross-platform Open Source Machine learning framework for .NET
Extensively used across Microsoft: Windows, Bing, Azure
High productivity throughout the entire ML workflow
Extensible to other frameworks (TensorFlow, CNTK…)
Learn more: BRK 3203 on Tuesday, May 8th at 1:15pm.

46. ML.NET Usage at Microsoft
+ more!
Windows 10
Power Point
Excel
Bing

47. Forecasting
Text Analytics
Supported on Windows, Linux, and macOS
Computer Vision
Azure ML Packages Preview
Python packages for Computer Vision, Forecasting and Text Analytics
Easily build and deploy models on Azure Machine Learning
Provides high level APIs for data preparation, augmentation, training,
evaluating and deployment.
Model experimentation and comparison, run history, model
management and deployment through Azure ML
Join at BRK 3226 What's new with Azure Machine Learning (breakout session)

49. Mark Hamilton
Software Engineer
github.com/mhamilton723

51. The Machine Learning framework made by and for .NET developers
Supported on Windows, Linux, and macOS
Azure ML and Project Brainwave
Real-time AI at cloud scale with industry-leading performance and lowest cost
Models are very easy to create and deploy as webservices
Record-setting DNN performance with accelerated ResNet50
Record speed: ResNet 50 on FPGA in <1.8 ms per image
Lowest cost: Only 21 cents per million images during preview
More accelerated models coming soon

53. FPGAs for ultra-fast inferencing
DATA PREP BUILD TRAIN DEPLOY
Stored on
Azure Premium Storage
Azure Machine
Learning
Jabil Classification Model
ResNet-50
Circuit board images
Jupyter Notebook
Azure Machine Learning
Ultra-fast Inferencing
using FPGAs

54. Ted Way, PhD
Senior Program Manager
@tedwinway

58. Planet scale Real-Time Inferencing for GeoAI
svFreely Available Imagery Labeled Training Data Inferred Land Cover Map

59. 415K
images/sec
FPGA
Real-Time Low-Latency Inferencing with FPGAs
Setup:
800 FPGAs on Azure
195 Million Images; 20TB
Real-time inferencing 1 image at a time
Results:
415K inferences/second @ 1.8ms latency
10.6 minutes total
Order of magnitude better Price/Perf
over CPU & GPU (V100 with TensorRT

60. BRK3202
BRK3205
BRK2508

62. What we demonstrated
Build Modern Applications with AI
1. Content based indexing, search and analysis
Build Conversational Applications
2. Engaging with users in natural ways through chats,
speech, voice and translation
Optimize Business Processes with AI
3. Building custom AI models for classification
4. Real-time AI with FPGAs and edge support
Cognitive Search
(Azure Search +
Azure Cognitive Services)
Azure
Bot Services
Azure
Machine Learning

63. Roger Luo,
President

65. Why you should develop your next AI app on Azure
1. Broadest set of Pre-Built AI capabilities
2. Customizable and flexible AI services
3. Most advanced conversational AI
4. Most differentiated support for AI @ the Edge + unique
hardware for AI
5. The strongest enterprise cloud for Data + AI