13. • Easy
• Roll your own with REST APIs
• Simple to add: just a few lines of code required
• Flexible
• Make the same API code call on iOS, Android, and Windows
• Integrate into the language and platform of your choice
• Tested
• Built by experts in their field from Microsoft Research, Bing, and Azure
Machine Learning
• Quality documentation, sample code, and community support
14. Create a seamless developer experience across desktop, cloud, or at the edge using Visual Studio AI Tools
Bot services
Infuse intelligence into your bot using
cognitive services
Speed development with a purpose-built
environment for bot creation
Integrate across multiple
channels to reach more customers
Cognitive services
Map complex
information and data
Use pre-built AI services
to solve business problems
Allow your apps to
process natural language
Azure search
Reduce complexity with
a fully-managed service
Get up and
running quickly
Use artificial intelligence
to extract insights
15.
16. Cognitive Services
Emotion
Computer Vision
Face
Video
Speaker Recognition
Speech
Custom Recognition
Translator
Linguistic Analysis
Language
Understanding
Bing Spell Check
WebLM
Text Analytics
Entity Linking
Knowledge
Exploration
Academic
Knowledge
Recommendations
Bing
Image Search
Bing
Video Search
Bing
Web Search
Bing
Autosuggest
Bing
News Search
37. LUIS [Language Understanding Intelligent Service]
Create your own LU model
Train by providing examples
Deploy to an HTTP endpoint
and activate on any device
Maintain model with ease
https://www.luis.ai/home
56. Domain specific pretrained models
To reduce time to market LanguageSpeech
…
SearchVision
Powerful infrastructure
To accelerate deep learning
CPU GPU FPGA
PyCharm Jupyter
Familiar Data Science tools
To simplify model development
Visual Studio Code Command line
Popular frameworks
To build advanced deep learning solutions
TensorFlowPytorch OnnxScikit-Learn
Azure
Databricks
Machine
Learning VMs
Azure Machine
Learning
Productive services
To empower data science and development teams
From the Intelligent Cloud to the Intelligent Edge
57. Azure AI Platform – Machine Learning
Easily build, deploy, and share predictive analytics solutions
• Simple, scalable, cutting edge. A fully managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions.
• Deploy in minutes. Azure Machine Learning means business. You can deploy your model into production as a web service that can be called
from any device, anywhere and that can use any data source.
• Publish, share, monetize. Share your solution with the world in the Gallery or on the Azure Marketplace.
Machine Learning
and Analytics
HDInsight
(Hadoop and
Spark)
Stream
Analytics
Data Lake
Analytics
Machine
Learning
58. A ZURE MACHINE LEARNING STUDIO
VISUAL DRAG-AND-DROP
A ZURE MACHINE LEARNING SERVICES
CODE-FIRST (TENSORFLOW, CNTK, ETC.)
Azure AI Platform - Machine Learning
for Engineers, Developers and Data Scientists
59. Vision
Computer Vision | Video Indexer | Face | Content Moderator
Speech
…
Speech to Text | Text to Speech | Speech Translation | Speaker Recognition
Language
Text Analytics | Spell Check | Language Understanding | Text Translation | QnA Maker
Bing
Search
Big Web Search | Video Search | Image Search | Visual Search | Entity Search |
News Search | Autosuggest
60. PyCharm Jupyter Visual Studio Code Command lineZeppelin
Interactive widgets for Jupyter Notebooks Azure Machine Learning for Visual Studio Code extension
63. What is Machine Learning?
Machine Learning
is a process by
which computers
find patterns in
data
Makes those
patterns available
to applications.
Application can
then gain insights
on new data based
on conformity to
the identified
patterns.
64. Why Machine Learning?
Enables you to learn from data faster than humans and make
decisions on new events.
Power of ML comes from the information that we can extract from
existing data in the system.
Enables to find patterns that are not obvious to humans.
It can self adapt to new events.
67. Microsoft & Machine Learning
Bing maps
launches
What’s the best
way to home?
Kinect
launches
What does that
motion “mean”?
Azure
Machine
Learning
What will happen
next?
Hotmail
launches
Which email is
junk?
Bing search
launches
Which searches
are most relevant?
Skype
Translator
launches
What is that
person saying?
201420091997 201520102008
75. Regression versus Classification
Regression problems
• Estimate household power
consumption
• Estimate customer’s income
Classification problems
• Power station will / will not meet
demand
• Customer will respond to
advertising
76. Common Classes of Algorithms
Classification Regression Anomaly
Detection
Clustering
77. Identifying to which of a set of categories (sub-populations) a new
observation belongs, on the basis of a training set of data containing
observations (or instances) whose category membership is known.
Classification
A set of statistical processes for estimating the relationships among
variables. It includes many techniques for modeling and analyzing several
variables, when the focus is on the relationship between a dependent variable
and one or more independent variables (or 'predictors’).
Regression
The task of grouping a set of objects in such a way that objects in the same
group (called a cluster) are more similar (in some sense) to each other than to
those in other groups (clusters).
Clustering
The identification of items, events or observations which do not conform to
an expected pattern or other items in a dataset. Typically the anomalous items
will translate to some kind of problem such as bank fraud, a structural defect,
medical problems or errors in a text.
Anomaly
Detection
Common Classes of Algorithms
82. 50°F 30°F 68°F 95°F1990
48°F 29°F 70°F 98°F2000
49°F 27°F 67°F 96°F2010
? ? ? ?2020
… … … ……
Known data
Model
Unknown data
Weather forecast
sample
Using known data, develop a model to predict unknown data.
83. 90°F
-26°F
50°F 30°F 68°F 95°F1990
48°F 29°F 70°F 98°F2000
49°F 27°F 67°F 96°F2010
Using known data, develop a model to predict unknown data.
Predict 2020 Summer
86. Binary versus Multiclass Classification
Multiclass examples
• kind of tree
• kind of network attack
• type of heart disease
Binary examples
• click prediction
• yes|no
• over|under
• win|loss
87. Example of a Classification Model (Decision Tree)
Age<30
Income >
$50K
Xbox-One
Customer
Not Xbox-One
Customer
Days Played >
728
Income >
$50K
Xbox-One
Customer
Not Xbox-One
Customer
Xbox-One
Customer
95. Data Science Process
20% of work
• Perform Statistical analysis
• Discover and handle outliers
• Prepare a list of predictive
model techniques
• Clearly defined business
problem
• Set success criteria
• Define clear data science
objectives
• Break business problems to
data science problems
• Identify machine learning
problem categories
• Understand data points and
constraints
• Formulate data analytics
strategy
• Perform required
transformation
• Experiment with multiple
models
• Choose the optimal model
• Create a feedback loop
Define
Business
Problem
Map to
Machine
Learning
Problem
Data
Preparation
Exploratory
Data
Analysis
Modeling Evaluation
80% of work
96. Build a Machine Learning Solution
Politics Sports Tech Health
Using known data, develop a model to predict unknown data.
97. Build a Machine Learning Solution
Using known data, develop a model to predict unknown data.
Documents Labels
Tech
Health
Politics
Politics
Sports
Documents consist of
unstructured text. Machine
learning typically assumes a
more structured format of
examples
98. Build a Machine Learning Solution
Using known data, develop a model to predict unknown data.
LabelsDocuments
Feature
Documents Labels
Tech
Health
Politics
Politics
Sports
99. Build a Machine Learning Solution
Known data
Data instance
{40, (180, 82), (11,7), 70, …..} : Healthy
Age Height/Weight
Blood Pressure
Hearth Rate
LabelFeatures
Feature Vector
100. Build a Machine Learning Solution
Using known data, develop a model to predict unknown data.
Documents Labels
Tech
Health
Politics
Politics
Sports
Training data
Train the
Model
Feature Vectors
Base Model
Adjust
Parameters
101. Build a Machine Learning Solution
Known data with true labels
Tech
Health
Politics
Politics
Sports
Tech
Health
Politics
Politics
Sports
Tech
Health
Politics
Politics
Sports
Model’s
Performance
Difference between
“True Labels” and
“Predicted Labels”
True
labels
Tech
Health
Politics
Politics
Sports
Predicted
labels
Train the Model
Split
Detach
+/-
+/-
+/-
112. Azure Notebooks – Azure Integration
Your Azure Subscription is fully integrated into Azure Notebooks
• Create a private notebook service for users of your Azure Subscription
• Create custom Docker images to run your notebooks
• Use premium computing resources to run your notebook (more cores, more RAM, GPU).
• Always-free compute tier available
Your Azure Subscription credentials available for single-sign-on
• Use Azure data resources (e.g., SQL Azure, Cosmos DB, Azure Blob and Table storage)
without needing to embed credentials in your notebooks
• Use Azure compute resources (e.g., Azure Batch, Azure Batch AI, Azure Machine Learning)
without needing to embed credentials in your notebooks
115. Azure Notebooks - Projects
Use this to:
Create New Projects
Upload OR Clone from GitHub
Migrate (Upload) a local Jupyter notebook
Share your Notebook
117. Azure Notebooks – Toolbar and Shortcuts
Mode What Shortcut
Command (Press Esc to enter) Run cell Shift-Enter
Command Add cell below B
Command Add cell above A
Command Delete a cell d-d
Command Go into edit mode Enter
Edit (Press Enter to enable) Run cell Shift-Enter
Edit Indent Clrl-]
Edit Unindent Ctrl-[
Edit Comment section Ctrl-/
Edit Function introspection Shift-Tab
124. Other Azure ML Examples
https://github.com/topics/azure-machine-learning
https://github.com/Azure/LearnAnalytics-
DoingMachineLearningwithAzureMLStudio/tree/master/instructor_resources
Image clustering using Python and Azure Machine Learning Studio
Workshop Azure ML Studio
https://academy.datachangers.com/courses/course-
v1:Microsoft+DAT275x+2018_T4/about
https://github.com/MicrosoftLearning/Principles-of-Machine-Learning-Python