Slides used during my session "Artificial Intelligence and Machine Learning in Azure" for The Azure Group (Canada's Azure User Community) on November 14 2018. Public group

1. Bruno Capuano
Innovation Lead @Avanade
@elbruno | http://elbruno.com

2. why should I care about AI and ML?
As a developer,

3. Some problems are difficult to solve using traditional algorithms and
procedural programming.

4. IBM slaps patent on coffee-delivering drones that
can read your MIND (link)

5. IBM slaps patent on coffee-delivering drones that
can read your MIND (link)

6. IBM coffee-delivering drones test footage

7. “It has exquisite buttons …
with long sleeves …works for
casual as well as business
settings”{f(x) {f(x)
Machine Learning: “Programming the
Unprogrammable”

8. f(x)
Model
Machine Learning creates a
Using this data
Machine Learning: “Programming the
UnProgrammable”

9. Is this A or B? How much? How many? How is this organized?
Regression ClusteringClassification
Machine Learning Tasks

10. Prepare Data Build & Train Evaluate
Azure Databricks Azure Machine Learning
Quickly launch and scale Spark on demand
Rich interactive workspace and notebooks
Seamless integration with all Azure data
services
Broad frameworks and tools support:
TensorFlow, Cognitive Toolkit, Caffe2, Keras,
MxNET, PyTorch
In the cloud – on the edge
Docker containers
Windows Machine Learning
Get started with machine learning

11. MakeMagicHappen();
https://www.avanade.com/AI

13. Azure Machine Learning Services
gives you an end-to-end
solution to prepare data and
train your model in the Cloud.
WinMLTools converts existing
models from CoreML, scikit-
learn, LIBSVM, and XGBoost
Azure Custom Vision makes it
easy to create your own image
models - https://customvision.ai/
Azure AI Gallery curates models
for use with Windows ML -
https://gallery.azure.ai/models
How do I get ONNX models to use in my
application?

14. Microsoft AI Platform

15. Microsoft AI platform
Azure AI Services
Azure Infrastructure
Tools

17. {
"tags":[
"train",
"platform",
"station",
"building",
"indoor",
"subway",
"track",
"walking",
"waiting",
"pulling",
"board",
"people",
"man",
"luggage",
"standing",
"holding",
"large",
"woman",
"yellow",
"suitcase"
],
"captions":[
{
"text":"people waiting
at a train
station",
"confidence":0.833099365
}
]
}
[
{
"name":"train",
"confidence":0.9975446
},
{
"name":"platform",
"confidence":0.995543063
},
{
"name":"station",
"confidence":0.9798007
},
{
"name":"indoor",
"confidence":0.927719653
},
{
"name":"subway",
"confidence":0.838939846
},
{
"name":"pulling",
"confidence":0.431715637
}
]
Computer Vision API

18. Cognitive Services
MakeMagicHappen();
https://www.avanade.com/AI

19. Azure Machine Learning Studio

20. Easy / Less Control Full Control / Harder
Vision Speech Language
Knowledge SearchLabs
TextAnalyticsAPI client = new TextAnalyticsAPI();
client.AzureRegion = AzureRegions.Westus;
client.SubscriptionKey = "1bf33391DeadFish";
client.Sentiment(
new MultiLanguageBatchInput(
new List<MultiLanguageInput>()
{
new MultiLanguageInput("en","0",
"This vacuum cleaner sucks so much dirt")
}));
e.g. Sentiment Analysis using Azure Cognitive Services
9% positive
Pre-built ML Models (Azure Cognitive Services)

21. 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
Azure Machine Learning Studio

22. Deploy the Model
Score and Evaluate the Model
Model the Data
Transform the Data
Clean the Data
Get the Data
Machine Learning Steps

23. Confusion Matrix
Truth
true false
Guess
positive
𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑓𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑡𝑝
𝑡𝑝 + 𝑓𝑝
negative
𝑓𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑡𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒
𝑟𝑒𝑐𝑎𝑙𝑙 =
𝑡𝑝
𝑡𝑝 + 𝑓𝑛
𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑡𝑝 + 𝑡𝑛
𝑡𝑝 + 𝑡𝑛 + 𝑓𝑝 + 𝑓𝑛

24. Azure Machine Learning
Studio: Diabetes Predictor
and Sentiment Analysis
MakeMagicHappen();
https://www.avanade.com/AI

25. Deploy Experiment as WebService

26. Deploy Experiment as WebService

27. AI Powered Spreadsheets

28. Azure Machine Learning
Notebooks

31. Azure Machine Learning
Notebooks
MakeMagicHappen();
https://www.avanade.com/AI

32. Azure Machine Learning
DataScience LifeCycle

33. How the Azure Machine Learning service works:
architecture and concepts

34. Microsoft's new "WSYP" Program

35. Azure Machine Learning Service

36. 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

37. 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

38. 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

39. 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
Manage models

40. Azure Machine Learning
Visual Studio Tools for AI

41. VS Code extension with deep integration to Azure
ML
End to end development environment, from new
project through training
Support for remote training
Job management
On top of all of the goodness of VS Code
(Python, Jupyter, Git, etc)
VS Code Tools for AI

42. 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

43. What’s new with Azure Machine Learning

44. Microsoft AI platform
Azure AI Services
Azure Infrastructure
Tools

45. Bruno Capuano
Innovation Lead @Avanade
@elbruno | http://elbruno.com