The document outlines various Microsoft AI solutions and tools, including pre-built APIs, custom machine learning models, and on-premises analytics for businesses. It highlights products such as Azure Machine Learning, Azure Cognitive Services, and .NET frameworks to aid developers in integrating AI capabilities into their applications. Additionally, it discusses initiatives like AI for Earth and AI for Accessibility that utilize AI to address global challenges and enhance capabilities for people with disabilities.

1. Microsoft AI

2. Microsoft AI
Innovation is what creates tomorrow
Oleksandr Krakovetskyi
CEO DevRain Solutions, Ph.D.
CTO DonorUA
@sashaeve
Microsoft Regional Director
Microsoft Artificial Intelligence MVP
fb.com/alex.krakovetskiy

3. About
1. CEO DevRain Solutions,
devrain.com
2. CTO ДонорUA, donor.ua
3. PhD in Computer Science
4. Microsoft Regional Director
5. Microsoft Artificial Intelligence
Most Valuable Professional

4. .NET Fest & AI

5. Microsoft AI
1. Pre-built (cloud). Tap into high-quality
RESTful intelligent APIs for Vision, Speech,
Language, Knowledge and Search.
2. Custom ML. Build and train own models
with a big range of tools.
3. On-premises. Analytics engine embedded
in SQL, standalone enterprise server for
predictive analysis.
4. Conversational UI. Build, connect, deploy,
and manage intelligent bots to naturally
interact with your users.
5. Tools and Data Science VMs. DSVMs are
Azure virtual machine images that are pre-
installed, configured and tested with
popular tools commonly used for data
analytics, machine learning and AI.
6. Programs and Labs. AI School, AI Labs, AI
for Earth, AI for Accessibility.
7. AI on data. Data storage and analytics (e.g.
Power BI).

6. Microsoft AI in the Cloud
Product What is it What you can do with it
Azure Machine Learning service Managed cloud service for ML Train, deploy, and manage models
in Azure using Python and CLI
Azure Machine Learning Studio Drag-and-drop visual interface for
ML
Build, experiment, and deploy
models using preconfigured
algorithms
Azure Databricks Spark-based analytics platform Build and deploy models and data
workflows
Azure Cognitive Services Azure services with pre-built AI and
ML models
Easily add intelligent features to
your apps
Azure Data Science Virtual Machine Virtual machine with pre-installed
data science tools
Develop ML solutions in a pre-
configured environment
Azure Batch AI Managed service to train and test
ML an AI models in Azure
Scale training process without
having to manage complex
infrastructure

7. Microsoft AI On-premises
Product What is it What you can do with it
SQL Server Machine Learning
Services
Analytics engine embedded in
SQL
Build and deploy models inside
SQL Server
Microsoft Machine Learning
Server
Standalone enterprise server for
predictive analysis
Build and deploy models with R
and Python

8. Microsoft AI Developer tools
Product What is it What you can do with it
ML.NET Open-source, cross-platform ML SDK Develop ML solutions for .NET
applications
Windows ML Windows 10 ML platform Evaluate trained models on a
Windows 10 device
The Team Data Science Process Agile, iterative data science
methodology to deliver predictive
analytics solutions and intelligent
applications efficiently
Contains a distillation of the best
practices and structures from
Microsoft and others in the industry
that facilitate the successful
implementation of data science
initiatives.
Visual Studio Code Tools for AI Build, test, and deploy deep learning
and AI solutions
Develop deep learning and AI
solutions across Windows and
MacOS
R Tools for Visual Studio Turn Visual Studio into a powerful R
development environment.
Build R applications with Visual
Studio.

9. Pre-built models
Azure Cognitive Services

10. Search
• Bing Web Search
• Bing Visual Search
• Bing Custom Search
• Bing Entity Search
• Bing Video Search
• Bing News Search
• Bing Image Search
• Bing Autosuggest
Enable apps and services to harness the
power of a Web-scale, ad-free search
engine with Search.
https://azure.microsoft.com/en-
us/services/cognitive-
services/directory/search/

11. Speech
• Speech to Text
(Preview)
• Speaker Recognition
(Preview)
• Text to Speech
(Preview)
• Speech Translation
(Preview)
Convert spoken language into text or
produce natural sounding speech from
text using standard (or customizable) voice
fonts.
https://azure.microsoft.com/en-
us/services/cognitive-
services/directory/speech/

12. Language
1. Text Analytics
• Named Entity Recognition
• Key phrase extraction
• Text sentiment analysis
2. Bing Spell Check
3. Translator Text
• Automatic language detection
• Automated text translation
• Customizable translation
4. Content Moderator
5. Language Understanding
Allow your apps to process natural
language, evaluate sentiment and
topics, and learn how to recognize
what users want.
https://azure.microsoft.com/en-
us/services/cognitive-
services/directory/lang/

13. Vision
1. Computer Vision
• Image classification
• Celebrity and landmark recognition
in images
• Handwriting recognition
• OCR
2. Face
• Emotion recognition
• Similar face recognition and
grouping in images
3. Video Indexer
4. Content Moderator
5. Custom Vision
State-of-the-art image processing
algorithms help you moderate
content automatically and build
more personalized apps by returning
smart insights about faces, images,
and emotions.
https://azure.microsoft.com/en-
us/services/cognitive-
services/directory/vision/

14. Emotion recognition
Analyze faces to detect a range of feelings.

15. Face verification
Check the
likelihood that
two faces
belong to the
same person.

16. Video Indexer
Unlock video insights

17. Seeing AI
A free app that narrates the world around you.
Designed for the low vision community, this
research project harnesses the power of AI to
describe people, text and objects.
References:
1. https://www.microsoft.com/en-us/seeing-
ai/
2. https://www.youtube.com/watch?v=R2mC
-NUAmMk

18. Cognitive Services Labs
1. Project Gesture
2. Project Ink Analysis
3. Project Local Insights
4. Project Event Tracking
5. Project Answer Search
6. Project URL Preview
7. Project Conversation Learner
8. Project Personality Chat
9. Project Knowledge Exploration
10. Project Academic Knowledge
11. Project Entity Linking
12. Project Anomaly Finder
13. Project Custom Decision
https://labs.cognitive.microsoft.com/en-us/project-custom-decision

19. Custom AI
ML.NET, Azure ML Studio

20. ML.NET
1. ML.NET is an open source and cross-platform machine learning framework.
2. Classification and clustering (e.g. text categorization and sentiment analysis), regression (e.g.
forecasting and price prediction).
3. Works on any platform supporting 64-bit .NET Core, including Windows, Linux, and macOS.
References:
1. https://github.com/dotnet/machinelearning/
2. https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet
3. https://www.microsoft.com/net/learn/machine-learning-and-ai/get-started-with-ml-dotnet-
tutorial
4. https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/

21. What is ML.NET?

22. ML.NET 0.6
1. New API for building and using machine learning models
2. Ability to score pre-trained ONNX Models
3. Significant performance improvements for model prediction, .NET type
system consistency, and more
https://blogs.msdn.microsoft.com/dotnet/2018/10/08/announcing-ml-net-0-6-
machine-learning-net/

23. ML.NET 0.6 ONNX support

24. Ольга Гавриш. Машинне навчання для
.NET розробників за допомогою ML.NET
Сергій Корж. ML.NET: використання
машинного навчання в звичайних .NET
проектах

25. Azure Machine Learning Studio
A GUI-based integrated development
environment for constructing and
operationalizing Machine Learning
workflow on Azure.
https://studio.azureml.net/

26. Azure ML Studio
1. Anomaly Detection
2. Classification
3. Clustering
4. Recommendation
5. Regression
6. Statistical Functions
7. Text Analytics
8. OpenCV Library

27. Azure Machine Learning Service
Use Azure Machine Learning service to train, deploy, and
manage ML models using Python and CLI at cloud scale.
Use Automated ML feature that identifies the best machine
learning pipelines for your labelled data.
https://azure.microsoft.com/en-us/blog/announcing-
automated-ml-capability-in-azure-machine-learning/

29. Language Understanding (LUIS.ai)
A machine learning-based service to build natural language into apps, bots, and IoT devices.
Quickly create enterprise-ready, custom models that continuously improve.

31. QnA Maker
Build, train and publish a
simple question and answer
bot based on FAQ URLs,
structured documents,
product manuals or editorial
content in minutes.
https://www.qnamaker.ai/

32. Microsoft Bot Framework
Build, connect, deploy, and manage intelligent
bots to naturally interact with your users on a
website, app, Cortana, Microsoft Teams, Skype,
Slack, Facebook Messenger, and more. Get
started quick with a complete bot building
environment, all while only paying for what you
use.
https://dev.botframework.com/

33. Microsoft Bot Framework

34. Bot Framework 4.0 (preview)
1. Improved Bot Framework Emulator
2. Bot Builder Dispatch
3. OAuth inside your bot
4. Project Conversation Learner
(Cognitive Services lab)
5. Project Personality Chat (Cognitive
Services lab)

35. Amazon Alexa
Alexa is Amazon’s cloud-based voice
service available on tens of millions of
devices from Amazon and third-party
device manufacturers. With Alexa, you can
build natural voice experiences that offer
customers a more intuitive way to interact
with the technology they use every day.
Our collection of tools, APIs, reference
solutions, and documentation make it easy
for anyone to build with Alexa.

36. Amazon Alexa

37. Data Science VMs
Comprehensive pre-configured
virtual machines for data science
modelling, development and
deployment.
https://azure.microsoft.com/en-
us/services/virtual-
machines/data-science-virtual-
machines/

38. Data Science VMs
1. Data Science Virtual Machine - Windows 2016
2. Data Science Virtual Machine for Linux (Ubuntu)
3. Deep Learning Virtual Machine
4. Data Science Virtual Machine - Windows 2012
5. Data Science Virtual Machine for Linux (CentOS)
6. Geo AI Data Science VM with ArcGIS

39. Data Science VMs
1. Preconfigured analytics desktop in
the cloud
2. Data science training and education
3. On-demand elastic capacity for
large-scale projects
4. Short-term experimentation and
evaluation
5. Deep learning

40. The Microsoft Cognitive Toolkit
The Microsoft Cognitive Toolkit (https://cntk.ai) is
a unified deep learning toolkit that describes
neural networks as a series of computational
steps via a directed graph.
https://docs.microsoft.com/en-us/cognitive-toolkit/setup-
cntk-on-your-machine
https://github.com/Microsoft/CNTK/

41. Microsoft Research Open Data
A collection of free datasets from Microsoft Research
to advance state-of-the-art research in areas such as
natural language processing, computer vision, and
domain specific sciences. Download or copy directly
to a cloud-based Data Science Virtual Machine for a
seamless development experience.
https://msropendata.com/

42. AI School
Dive in and learn how to start building intelligence into your
solutions with the Microsoft AI platform, including pre-trained
AI services like Cognitive Services and Bot Framework, as well
as deep learning tools like Azure Machine Learning, Visual
Studio Code Tools for AI, and Cognitive Toolkit. Our platform
enables any developer to code in any language and infuse AI
into your apps. Whether your solutions are existing or new,
this is the intelligence platform to build on.
https://aischool.microsoft.com/en-us

43. Microsoft AI Lab
Experience, learn and code the latest
breakthrough AI innovations by
Microsoft.
https://www.ailab.microsoft.com/

44. Sketch2Code
An AI solution that
converts hand-written
drawings to working
HTML prototypes.
https://www.ailab.microsoft.com/experiments/30c61484-d081-4072-99d6-e132d362b99d

45. AI for Earth
AI for Earth puts Microsoft’s cloud and AI tools in the hands of those
working to solve global environmental challenges. Through grants that
provide access to cloud and AI tools, opportunities for education and
training on AI, and investments in innovative, scalable solutions, AI for
Earth works to advance sustainability across the globe. To learn about
Microsoft’s broader sustainability mission, visit Microsoft Green.
https://www.microsoft.com/en-us/aiforearth/

46. AI for Accessibility
A $25 million program that harnesses the
power of AI to amplify human capability
for the more than one billion people
around the world with a disability.
https://www.microsoft.com/en-us/ai-for-
accessibility

47. Q&A
Oleksandr Krakovetskyi
alex.krakovetskiy@devrain.com
@sashaeve
fb.com/alex.krakovetskiy