SlideShare ist ein Scribd-Unternehmen logo
1 von 50
Downloaden Sie, um offline zu lesen
Azure Machine Learning
(Data Camp)
Mostafa Elzoghbi
Sr. Technical Evangelist – Microsoft
@MostafaElzoghbi
http://mostafa.rocks
Session Objectives & Takeaways
• What is Data Science?
• What is Machine Learning?
• Why do we need Machine Learning?
• Azure ML studio platform capabilities.
• Machine Learning basic concepts & algorithms:
• How to build a model
• Supervised vs Unsupervised models
• Evaluate a model.
• ML algorithms: Regression, classification, clustering and
recommendations.
Data Science Involves
• Data science is about using data to make decision that drive actions.
• Data science process involves:
• Data selection
• Preprocessing
• Transformation
• Data Mining
• Delivering value from data : Interpretation and evaluation
Data Science
• Data Science is far too complex
• Cost of accessing/using efficient ML algorithms is high
• Comprehensive knowledge required on different tools/platforms to develop
a complete ML project
• Difficult to put the developed solution into a scalable production stage
• Need a simpler/scalable method:
Azure Machine Learning Service
What is Machine Learning ?
• Using known data, develop a model to predict unknown data.
Known Data: Big enough archive, previous observations, past data
Model: Known data + Algorithms (ML algorithms)
Unknown Data: Missing, Unseen, not existing, future data
Microsoft & Machine Learning
Bing maps
launches
What’s the best
way to home?
Kinect launches
What does that
motion
“mean”?
Azure Machine
Learning GA
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
Why Machine Learning?
Machine learning enables nearly every
value proposition of web search.
Image Analyze
Accent Color: Which border color is the best?
Accent Color: Analyze Image
Accent Color: Windows 10 Store
Accent Color: Windows 10 Store
Text Analytics: User reviews
Positive Negative
Microsoft Azure Machine Learning
Make machine learning accessible to every enterprise,
data scientist, developer, information worker, consumer,
and device anywhere in the world.
From data to decisions and actions
Value
Transform data into intelligent action
DATA
Business
apps
Custom
apps
Sensors
and devices
INTELLIGENCE ACTION
People
Automated
Systems
Microsoft Azure Machine Learning
• Web based UI accessible from different browsers
• Share|collaborate to any other ML workspace
• Drag & Drop visual design|development
• Wide range of ML Algorithms catalog
• Extend with OSS R|Python scripts
• Share|Document with IPython|Jupyter
• Deploy|Publish|Scale rapidly (APIs)
Fully
managed
Integrated Flexible Deploy in
minutes
No software to install, no
hardware tomanage, all
you need is an
Azure subscription.
Drag, drop and connect
interface. Data sources
with just a drop down; run
across any data.
Built-in collection of best
of breed algorithms with
no coding required. Drop
in custom R or use popular
CRAN packages.
Operationalize models as
web services with a
single click.
Monetize in Machine
Learning Marketplace.
Azure Machine Learning Ecosystem
Get/Prepare
Data
Build/Edit
Experiment
Create/Update
Model
Evaluate
Model
Results
Publish Web
Service
Build ML Model Deploy as Web ServiceProvision Workspace
Get Azure
Subscription
Create
Workspace
Publish an App
Azure Data
Marketplace
Blobs and Tables
Hadoop (HDInsight)
Relational DB (Azure SQL DB)
Data Clients
Model is now a web
service that is callable
Monetize the API through
our marketplace
API
Integrated development
environment for Machine
Learning
ML STUDIO
DEMO
Azure Machine Learning Studio
EXAMPLE
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.
Model (Regression)
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
EXAMPLE
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
EXAMPLE
Classify a news article as (politics, sports, technology, health, …)
Politics Sports Tech Health
Model (Classification)
Using known data, develop a model to predict unknown data.
Known data (Training data)
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
Process the raw data
Known data (Training data)
Using known data, develop a model to predict unknown data.
LabelsDocuments
Feature
Documents Labels
Tech
Health
Politics
Politics
Sports
Process each data instance to represent it as a feature vector
Feature vector
Known data
Data instance
i.e.
{40, (180, 82), (11,7), 70, …..} : Healthy
Age Height/Weight
Blood Pressure
Hearth Rate
LabelFeatures
Feature Vector
Developing a Model
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
Model’s Performance
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
+/-
+/-
+/-
Steps to Build a Machine Learning Solution
1
Problem
Framing
2
Get/Prepare
Data
3
Develop
Model
4
Deploy
Model
5
Evaluate /
Track
Performance
3.1
Analysis/
Metric
definition
3.2
Feature
Engineering
3.3
Model
Training
3.4
Parameter
Tuning
3.5
Evaluation
Example use cases
Machine Learning Algorithms
• ML Algorithm defines how your model will react
• Which Algorithm to use? Depends on:
• Data Quality
• Data Size
• What you want to predict
• Time constraint
• Computation power
• Memory limits
Machine Learning Algorithms
You can develop solutions by using
• Custom algorithms written in R | Python
• Ready to use ML services from data market
• Existing algorithms
Machine Learning Algorithms
Two major category of algorithms
• Supervised
• Unsupervised
Most commonly used machine learning algorithms are supervised
(requires labels)
• Supervised learning examples
• This customer will like coffee
• This network traffic indicates a
denial of service attack
• Unsupervised learning examples
• These customers are similar
• This network traffic is unusual
Common Classes of Algorithms
(Supervised|Unsupervised)
Classification Regression Anomaly
Detection
Clustering
Supervised Supervised SupervisedUnSupervised
Why you need to know these algorithms?
• If you want to answer a YES|NO question, it is classification
• If you want to predict a numerical value, it is regression
• If you want to group data into similar observations, it is clustering
• If you want to recommend an item, it is recommender system
• If you want to find anomalies in a group, it is anomaly detection
and many other ML algorithms for specific problem
Classification
Scenarios:
 Which customer are more likely to buy, stay, leave (churn analysis)
 Which transactions|actions are fraudulent
 Which quotes are more likely to become orders
 Recognition of patterns: speech, speaker, image, movement, etc.
Algorithms: Boosted Decision Tree, Decision Forest, Decision
Jungle, Logistic Regression, SVM, ANN, etc.
Classification
Clustering
Scenarios:
 Customer segmentation: divide a customer base into groups of
individuals that are similar in specific ways relevant to marketing, such
as age, gender, interests, spending habits, etc.
 Market segmentation
 Quantization of all sorts, such as, data compression, color reduction,
etc.
 Pattern recognition
Algorithms: K-means
Clustering
Regression
Scenarios:
 Stock prices prediction
 Sales forecasts
 Premiums on insurance based on different factors
 Quality control: number of complaints over time based on product
specs, utilization, etc.
 Workforce prediction
 Workload prediction
Algorithms: Bayesian Linear, Linear Regression, Ordinal
Regression, ANN, Boosted Decision Tree, Decision Forest
Regression
Regression versus Classification
Does your customer want to predict|estimate a number (regression)
or apply a label|categorize (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
Binary versus Multiclass Classification
Does your customer want a yes|no answer?
• Binary examples
• click prediction
• yes|no
• over|under
• win|loss
• Multiclass examples
• kind of tree
• kind of network attack
• type of heart disease
DEMO
Machine Learning Basics Infographic
References
• Free e-book “Azure Machine Learning”
• https://mva.microsoft.com/ebooks#9780735698178
• Azure Machine Learning documentation
• https://azure.microsoft.com/en-us/documentation/services/machine-
learning/
• Data Science and Machine Learning Essentials
• www.edx.org
• Azure ML HOL (GitHub):
• https://github.com/Azure-Readiness/hol-azure-machine-learning/
HOL Document
• Access Azure HOL Doc: https://aka.ms/azuremlhol
Thank you
• Check out my blog for Azure ML articles: http://mostafa.rocks
• Follow me on Twitter: @MostafaElzoghbi

Weitere ähnliche Inhalte

Was ist angesagt?

Azure Machine Learning tutorial
Azure Machine Learning tutorialAzure Machine Learning tutorial
Azure Machine Learning tutorialGiacomo Lanciano
 
Machine Learning and AI
Machine Learning and AIMachine Learning and AI
Machine Learning and AIJames Serra
 
Google Cloud Machine Learning
 Google Cloud Machine Learning  Google Cloud Machine Learning
Google Cloud Machine Learning India Quotient
 
From Data Science to MLOps
From Data Science to MLOpsFrom Data Science to MLOps
From Data Science to MLOpsCarl W. Handlin
 
Discover AI with Microsoft Azure
Discover AI with Microsoft AzureDiscover AI with Microsoft Azure
Discover AI with Microsoft AzureJürgen Ambrosi
 
Azure DataBricks for Data Engineering by Eugene Polonichko
Azure DataBricks for Data Engineering by Eugene PolonichkoAzure DataBricks for Data Engineering by Eugene Polonichko
Azure DataBricks for Data Engineering by Eugene PolonichkoDimko Zhluktenko
 
Building Modern Data Platform with Microsoft Azure
Building Modern Data Platform with Microsoft AzureBuilding Modern Data Platform with Microsoft Azure
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
 
Introducing Azure SQL Database
Introducing Azure SQL DatabaseIntroducing Azure SQL Database
Introducing Azure SQL DatabaseJames Serra
 
MLOps Using MLflow
MLOps Using MLflowMLOps Using MLflow
MLOps Using MLflowDatabricks
 
Ml ops past_present_future
Ml ops past_present_futureMl ops past_present_future
Ml ops past_present_futureNisha Talagala
 
Top 10 Applications of Machine Learning | Machine Learning Application Exampl...
Top 10 Applications of Machine Learning | Machine Learning Application Exampl...Top 10 Applications of Machine Learning | Machine Learning Application Exampl...
Top 10 Applications of Machine Learning | Machine Learning Application Exampl...Edureka!
 
Azure Databricks – Customer Experiences and Lessons Denzil Ribeiro Madhu Ganta
Azure Databricks – Customer Experiences and Lessons Denzil Ribeiro Madhu GantaAzure Databricks – Customer Experiences and Lessons Denzil Ribeiro Madhu Ganta
Azure Databricks – Customer Experiences and Lessons Denzil Ribeiro Madhu GantaDatabricks
 
ETL Made Easy with Azure Data Factory and Azure Databricks
ETL Made Easy with Azure Data Factory and Azure DatabricksETL Made Easy with Azure Data Factory and Azure Databricks
ETL Made Easy with Azure Data Factory and Azure DatabricksDatabricks
 
AWS ML Model Deployment
AWS ML Model DeploymentAWS ML Model Deployment
AWS ML Model DeploymentKnoldus Inc.
 
MLOps Virtual Event | Building Machine Learning Platforms for the Full Lifecycle
MLOps Virtual Event | Building Machine Learning Platforms for the Full LifecycleMLOps Virtual Event | Building Machine Learning Platforms for the Full Lifecycle
MLOps Virtual Event | Building Machine Learning Platforms for the Full LifecycleDatabricks
 

Was ist angesagt? (20)

MLOps in action
MLOps in actionMLOps in action
MLOps in action
 
Azure Machine Learning tutorial
Azure Machine Learning tutorialAzure Machine Learning tutorial
Azure Machine Learning tutorial
 
What is MLOps
What is MLOpsWhat is MLOps
What is MLOps
 
Machine Learning and AI
Machine Learning and AIMachine Learning and AI
Machine Learning and AI
 
Google Cloud Machine Learning
 Google Cloud Machine Learning  Google Cloud Machine Learning
Google Cloud Machine Learning
 
From Data Science to MLOps
From Data Science to MLOpsFrom Data Science to MLOps
From Data Science to MLOps
 
Discover AI with Microsoft Azure
Discover AI with Microsoft AzureDiscover AI with Microsoft Azure
Discover AI with Microsoft Azure
 
Azure DataBricks for Data Engineering by Eugene Polonichko
Azure DataBricks for Data Engineering by Eugene PolonichkoAzure DataBricks for Data Engineering by Eugene Polonichko
Azure DataBricks for Data Engineering by Eugene Polonichko
 
MLOps for production-level machine learning
MLOps for production-level machine learningMLOps for production-level machine learning
MLOps for production-level machine learning
 
MLOps.pptx
MLOps.pptxMLOps.pptx
MLOps.pptx
 
Building Modern Data Platform with Microsoft Azure
Building Modern Data Platform with Microsoft AzureBuilding Modern Data Platform with Microsoft Azure
Building Modern Data Platform with Microsoft Azure
 
Introducing Azure SQL Database
Introducing Azure SQL DatabaseIntroducing Azure SQL Database
Introducing Azure SQL Database
 
Ml ops on AWS
Ml ops on AWSMl ops on AWS
Ml ops on AWS
 
MLOps Using MLflow
MLOps Using MLflowMLOps Using MLflow
MLOps Using MLflow
 
Ml ops past_present_future
Ml ops past_present_futureMl ops past_present_future
Ml ops past_present_future
 
Top 10 Applications of Machine Learning | Machine Learning Application Exampl...
Top 10 Applications of Machine Learning | Machine Learning Application Exampl...Top 10 Applications of Machine Learning | Machine Learning Application Exampl...
Top 10 Applications of Machine Learning | Machine Learning Application Exampl...
 
Azure Databricks – Customer Experiences and Lessons Denzil Ribeiro Madhu Ganta
Azure Databricks – Customer Experiences and Lessons Denzil Ribeiro Madhu GantaAzure Databricks – Customer Experiences and Lessons Denzil Ribeiro Madhu Ganta
Azure Databricks – Customer Experiences and Lessons Denzil Ribeiro Madhu Ganta
 
ETL Made Easy with Azure Data Factory and Azure Databricks
ETL Made Easy with Azure Data Factory and Azure DatabricksETL Made Easy with Azure Data Factory and Azure Databricks
ETL Made Easy with Azure Data Factory and Azure Databricks
 
AWS ML Model Deployment
AWS ML Model DeploymentAWS ML Model Deployment
AWS ML Model Deployment
 
MLOps Virtual Event | Building Machine Learning Platforms for the Full Lifecycle
MLOps Virtual Event | Building Machine Learning Platforms for the Full LifecycleMLOps Virtual Event | Building Machine Learning Platforms for the Full Lifecycle
MLOps Virtual Event | Building Machine Learning Platforms for the Full Lifecycle
 

Ähnlich wie Azure Machine Learning

Machine Learning Classifiers
Machine Learning ClassifiersMachine Learning Classifiers
Machine Learning ClassifiersMostafa
 
Building predictive models in Azure Machine Learning
Building predictive models in Azure Machine LearningBuilding predictive models in Azure Machine Learning
Building predictive models in Azure Machine LearningMostafa
 
K-MUG Azure Machine Learning
K-MUG Azure Machine LearningK-MUG Azure Machine Learning
K-MUG Azure Machine LearningPraveen Nair
 
How an AI-backed recommendation system can help increase revenue for your onl...
How an AI-backed recommendation system can help increase revenue for your onl...How an AI-backed recommendation system can help increase revenue for your onl...
How an AI-backed recommendation system can help increase revenue for your onl...Skyl.ai
 
Gse uk-cedrinemadera-2018-shared
Gse uk-cedrinemadera-2018-sharedGse uk-cedrinemadera-2018-shared
Gse uk-cedrinemadera-2018-sharedcedrinemadera
 
DevOps for Machine Learning overview en-us
DevOps for Machine Learning overview en-usDevOps for Machine Learning overview en-us
DevOps for Machine Learning overview en-useltonrodriguez11
 
It’s all about me_ From big data models to personalized experience Presentation
It’s all about me_ From big data models to personalized experience PresentationIt’s all about me_ From big data models to personalized experience Presentation
It’s all about me_ From big data models to personalized experience PresentationYao H. Morin, Ph.D.
 
Building enterprise advance analytics platform
Building enterprise advance analytics platformBuilding enterprise advance analytics platform
Building enterprise advance analytics platformHaoran Du
 
Machine Learning Models in Production
Machine Learning Models in ProductionMachine Learning Models in Production
Machine Learning Models in ProductionDataWorks Summit
 
ChatGPT and not only: how can you use the power of Generative AI at scale
ChatGPT and not only: how can you use the power of Generative AI at scaleChatGPT and not only: how can you use the power of Generative AI at scale
ChatGPT and not only: how can you use the power of Generative AI at scaleMaxim Salnikov
 
Spsbepoelmanssharepointbigdataclean 150421080105-conversion-gate02
Spsbepoelmanssharepointbigdataclean 150421080105-conversion-gate02Spsbepoelmanssharepointbigdataclean 150421080105-conversion-gate02
Spsbepoelmanssharepointbigdataclean 150421080105-conversion-gate02BIWUG
 
How to build your own Delve: combining machine learning, big data and SharePoint
How to build your own Delve: combining machine learning, big data and SharePointHow to build your own Delve: combining machine learning, big data and SharePoint
How to build your own Delve: combining machine learning, big data and SharePointJoris Poelmans
 
JoTechies -Azure Machine Learning
JoTechies -Azure Machine LearningJoTechies -Azure Machine Learning
JoTechies -Azure Machine LearningJoTechies
 
Self-Service Analytics Framework - Connected Brains 2018
Self-Service Analytics Framework - Connected Brains 2018Self-Service Analytics Framework - Connected Brains 2018
Self-Service Analytics Framework - Connected Brains 2018LoQutus
 
Microsoft DevOps for AI with GoDataDriven
Microsoft DevOps for AI with GoDataDrivenMicrosoft DevOps for AI with GoDataDriven
Microsoft DevOps for AI with GoDataDrivenGoDataDriven
 
test - Future of Ecommerce: How to Improve the Online Shopping Experience Usi...
test - Future of Ecommerce: How to Improve the Online Shopping Experience Usi...test - Future of Ecommerce: How to Improve the Online Shopping Experience Usi...
test - Future of Ecommerce: How to Improve the Online Shopping Experience Usi...Skyl.ai
 
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...Ali Alkan
 
Conceptual vs. Logical vs. Physical Data Modeling
Conceptual vs. Logical vs. Physical Data ModelingConceptual vs. Logical vs. Physical Data Modeling
Conceptual vs. Logical vs. Physical Data ModelingDATAVERSITY
 

Ähnlich wie Azure Machine Learning (20)

Machine Learning Classifiers
Machine Learning ClassifiersMachine Learning Classifiers
Machine Learning Classifiers
 
Building predictive models in Azure Machine Learning
Building predictive models in Azure Machine LearningBuilding predictive models in Azure Machine Learning
Building predictive models in Azure Machine Learning
 
K-MUG Azure Machine Learning
K-MUG Azure Machine LearningK-MUG Azure Machine Learning
K-MUG Azure Machine Learning
 
Machine learning
Machine learningMachine learning
Machine learning
 
How an AI-backed recommendation system can help increase revenue for your onl...
How an AI-backed recommendation system can help increase revenue for your onl...How an AI-backed recommendation system can help increase revenue for your onl...
How an AI-backed recommendation system can help increase revenue for your onl...
 
Gse uk-cedrinemadera-2018-shared
Gse uk-cedrinemadera-2018-sharedGse uk-cedrinemadera-2018-shared
Gse uk-cedrinemadera-2018-shared
 
DevOps for Machine Learning overview en-us
DevOps for Machine Learning overview en-usDevOps for Machine Learning overview en-us
DevOps for Machine Learning overview en-us
 
It’s all about me_ From big data models to personalized experience Presentation
It’s all about me_ From big data models to personalized experience PresentationIt’s all about me_ From big data models to personalized experience Presentation
It’s all about me_ From big data models to personalized experience Presentation
 
Building enterprise advance analytics platform
Building enterprise advance analytics platformBuilding enterprise advance analytics platform
Building enterprise advance analytics platform
 
Machine Learning Models in Production
Machine Learning Models in ProductionMachine Learning Models in Production
Machine Learning Models in Production
 
ChatGPT and not only: how can you use the power of Generative AI at scale
ChatGPT and not only: how can you use the power of Generative AI at scaleChatGPT and not only: how can you use the power of Generative AI at scale
ChatGPT and not only: how can you use the power of Generative AI at scale
 
Spsbepoelmanssharepointbigdataclean 150421080105-conversion-gate02
Spsbepoelmanssharepointbigdataclean 150421080105-conversion-gate02Spsbepoelmanssharepointbigdataclean 150421080105-conversion-gate02
Spsbepoelmanssharepointbigdataclean 150421080105-conversion-gate02
 
How to build your own Delve: combining machine learning, big data and SharePoint
How to build your own Delve: combining machine learning, big data and SharePointHow to build your own Delve: combining machine learning, big data and SharePoint
How to build your own Delve: combining machine learning, big data and SharePoint
 
JoTechies -Azure Machine Learning
JoTechies -Azure Machine LearningJoTechies -Azure Machine Learning
JoTechies -Azure Machine Learning
 
Self-Service Analytics Framework - Connected Brains 2018
Self-Service Analytics Framework - Connected Brains 2018Self-Service Analytics Framework - Connected Brains 2018
Self-Service Analytics Framework - Connected Brains 2018
 
Data engineering design patterns
Data engineering design patternsData engineering design patterns
Data engineering design patterns
 
Microsoft DevOps for AI with GoDataDriven
Microsoft DevOps for AI with GoDataDrivenMicrosoft DevOps for AI with GoDataDriven
Microsoft DevOps for AI with GoDataDriven
 
test - Future of Ecommerce: How to Improve the Online Shopping Experience Usi...
test - Future of Ecommerce: How to Improve the Online Shopping Experience Usi...test - Future of Ecommerce: How to Improve the Online Shopping Experience Usi...
test - Future of Ecommerce: How to Improve the Online Shopping Experience Usi...
 
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
 
Conceptual vs. Logical vs. Physical Data Modeling
Conceptual vs. Logical vs. Physical Data ModelingConceptual vs. Logical vs. Physical Data Modeling
Conceptual vs. Logical vs. Physical Data Modeling
 

Mehr von Mostafa

The role of intelligent sensors in the cloud public
The role of intelligent sensors in the cloud publicThe role of intelligent sensors in the cloud public
The role of intelligent sensors in the cloud publicMostafa
 
Skill up in machine learning using Azure ML
Skill up in machine learning using Azure MLSkill up in machine learning using Azure ML
Skill up in machine learning using Azure MLMostafa
 
Big data talking stories in Healthcare
Big data talking stories in Healthcare Big data talking stories in Healthcare
Big data talking stories in Healthcare Mostafa
 
Architecting big data solutions in the cloud
Architecting big data solutions in the cloudArchitecting big data solutions in the cloud
Architecting big data solutions in the cloudMostafa
 
Programming in Spark using PySpark
Programming in Spark using PySpark      Programming in Spark using PySpark
Programming in Spark using PySpark Mostafa
 
Introducing Power BI Embedded
Introducing Power BI EmbeddedIntroducing Power BI Embedded
Introducing Power BI EmbeddedMostafa
 
Extending Product Outreach with Outlook Connectors
Extending Product Outreach with Outlook ConnectorsExtending Product Outreach with Outlook Connectors
Extending Product Outreach with Outlook ConnectorsMostafa
 
Build intelligent solutions using Azure
Build intelligent solutions using AzureBuild intelligent solutions using Azure
Build intelligent solutions using AzureMostafa
 
Big data solutions in Azure
Big data solutions in AzureBig data solutions in Azure
Big data solutions in AzureMostafa
 
Building Big data solutions in Azure
Building Big data solutions in AzureBuilding Big data solutions in Azure
Building Big data solutions in AzureMostafa
 
Patterns and Practices in Building Office Add-ins
Patterns and Practices in Building Office Add-insPatterns and Practices in Building Office Add-ins
Patterns and Practices in Building Office Add-insMostafa
 
Data science essentials in azure ml
Data science essentials in azure mlData science essentials in azure ml
Data science essentials in azure mlMostafa
 
Build Interactive Analytics using Power BI
Build Interactive Analytics using Power BIBuild Interactive Analytics using Power BI
Build Interactive Analytics using Power BIMostafa
 
TypeScript Jump Start
TypeScript Jump StartTypeScript Jump Start
TypeScript Jump StartMostafa
 
Big data solutions in azure
Big data solutions in azureBig data solutions in azure
Big data solutions in azureMostafa
 
Build intelligent solutions using ms azure
Build intelligent solutions using ms azureBuild intelligent solutions using ms azure
Build intelligent solutions using ms azureMostafa
 
Mistakes that kill startups
Mistakes that kill startupsMistakes that kill startups
Mistakes that kill startupsMostafa
 
PnP in building office add ins - public
PnP in building office add ins - publicPnP in building office add ins - public
PnP in building office add ins - publicMostafa
 
How to migrate Console Apps as a cloud service
How to migrate Console Apps as a cloud serviceHow to migrate Console Apps as a cloud service
How to migrate Console Apps as a cloud serviceMostafa
 
HBase introduction in azure
HBase introduction in azureHBase introduction in azure
HBase introduction in azureMostafa
 

Mehr von Mostafa (20)

The role of intelligent sensors in the cloud public
The role of intelligent sensors in the cloud publicThe role of intelligent sensors in the cloud public
The role of intelligent sensors in the cloud public
 
Skill up in machine learning using Azure ML
Skill up in machine learning using Azure MLSkill up in machine learning using Azure ML
Skill up in machine learning using Azure ML
 
Big data talking stories in Healthcare
Big data talking stories in Healthcare Big data talking stories in Healthcare
Big data talking stories in Healthcare
 
Architecting big data solutions in the cloud
Architecting big data solutions in the cloudArchitecting big data solutions in the cloud
Architecting big data solutions in the cloud
 
Programming in Spark using PySpark
Programming in Spark using PySpark      Programming in Spark using PySpark
Programming in Spark using PySpark
 
Introducing Power BI Embedded
Introducing Power BI EmbeddedIntroducing Power BI Embedded
Introducing Power BI Embedded
 
Extending Product Outreach with Outlook Connectors
Extending Product Outreach with Outlook ConnectorsExtending Product Outreach with Outlook Connectors
Extending Product Outreach with Outlook Connectors
 
Build intelligent solutions using Azure
Build intelligent solutions using AzureBuild intelligent solutions using Azure
Build intelligent solutions using Azure
 
Big data solutions in Azure
Big data solutions in AzureBig data solutions in Azure
Big data solutions in Azure
 
Building Big data solutions in Azure
Building Big data solutions in AzureBuilding Big data solutions in Azure
Building Big data solutions in Azure
 
Patterns and Practices in Building Office Add-ins
Patterns and Practices in Building Office Add-insPatterns and Practices in Building Office Add-ins
Patterns and Practices in Building Office Add-ins
 
Data science essentials in azure ml
Data science essentials in azure mlData science essentials in azure ml
Data science essentials in azure ml
 
Build Interactive Analytics using Power BI
Build Interactive Analytics using Power BIBuild Interactive Analytics using Power BI
Build Interactive Analytics using Power BI
 
TypeScript Jump Start
TypeScript Jump StartTypeScript Jump Start
TypeScript Jump Start
 
Big data solutions in azure
Big data solutions in azureBig data solutions in azure
Big data solutions in azure
 
Build intelligent solutions using ms azure
Build intelligent solutions using ms azureBuild intelligent solutions using ms azure
Build intelligent solutions using ms azure
 
Mistakes that kill startups
Mistakes that kill startupsMistakes that kill startups
Mistakes that kill startups
 
PnP in building office add ins - public
PnP in building office add ins - publicPnP in building office add ins - public
PnP in building office add ins - public
 
How to migrate Console Apps as a cloud service
How to migrate Console Apps as a cloud serviceHow to migrate Console Apps as a cloud service
How to migrate Console Apps as a cloud service
 
HBase introduction in azure
HBase introduction in azureHBase introduction in azure
HBase introduction in azure
 

Kürzlich hochgeladen

Growing Oxen: channel operators and retries
Growing Oxen: channel operators and retriesGrowing Oxen: channel operators and retries
Growing Oxen: channel operators and retriesSoftwareMill
 
Enterprise Document Management System - Qualityze Inc
Enterprise Document Management System - Qualityze IncEnterprise Document Management System - Qualityze Inc
Enterprise Document Management System - Qualityze Incrobinwilliams8624
 
Leveraging DxSherpa's Generative AI Services to Unlock Human-Machine Harmony
Leveraging DxSherpa's Generative AI Services to Unlock Human-Machine HarmonyLeveraging DxSherpa's Generative AI Services to Unlock Human-Machine Harmony
Leveraging DxSherpa's Generative AI Services to Unlock Human-Machine Harmonyelliciumsolutionspun
 
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...Jaydeep Chhasatia
 
Your Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
Your Vision, Our Expertise: TECUNIQUE's Tailored Software TeamsYour Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
Your Vision, Our Expertise: TECUNIQUE's Tailored Software TeamsJaydeep Chhasatia
 
Streamlining Your Application Builds with Cloud Native Buildpacks
Streamlining Your Application Builds  with Cloud Native BuildpacksStreamlining Your Application Builds  with Cloud Native Buildpacks
Streamlining Your Application Builds with Cloud Native BuildpacksVish Abrams
 
Fields in Java and Kotlin and what to expect.pptx
Fields in Java and Kotlin and what to expect.pptxFields in Java and Kotlin and what to expect.pptx
Fields in Java and Kotlin and what to expect.pptxJoão Esperancinha
 
eAuditor Audits & Inspections - conduct field inspections
eAuditor Audits & Inspections - conduct field inspectionseAuditor Audits & Inspections - conduct field inspections
eAuditor Audits & Inspections - conduct field inspectionsNirav Modi
 
Watermarking in Source Code: Applications and Security Challenges
Watermarking in Source Code: Applications and Security ChallengesWatermarking in Source Code: Applications and Security Challenges
Watermarking in Source Code: Applications and Security ChallengesShyamsundar Das
 
Deep Learning for Images with PyTorch - Datacamp
Deep Learning for Images with PyTorch - DatacampDeep Learning for Images with PyTorch - Datacamp
Deep Learning for Images with PyTorch - DatacampVICTOR MAESTRE RAMIREZ
 
Introduction-to-Software-Development-Outsourcing.pptx
Introduction-to-Software-Development-Outsourcing.pptxIntroduction-to-Software-Development-Outsourcing.pptx
Introduction-to-Software-Development-Outsourcing.pptxIntelliSource Technologies
 
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/ML
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/MLBig Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/ML
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/MLAlluxio, Inc.
 
Generative AI for Cybersecurity - EC-Council
Generative AI for Cybersecurity - EC-CouncilGenerative AI for Cybersecurity - EC-Council
Generative AI for Cybersecurity - EC-CouncilVICTOR MAESTRE RAMIREZ
 
IA Generativa y Grafos de Neo4j: RAG time
IA Generativa y Grafos de Neo4j: RAG timeIA Generativa y Grafos de Neo4j: RAG time
IA Generativa y Grafos de Neo4j: RAG timeNeo4j
 
JS-Experts - Cybersecurity for Generative AI
JS-Experts - Cybersecurity for Generative AIJS-Experts - Cybersecurity for Generative AI
JS-Experts - Cybersecurity for Generative AIIvo Andreev
 
online pdf editor software solutions.pdf
online pdf editor software solutions.pdfonline pdf editor software solutions.pdf
online pdf editor software solutions.pdfMeon Technology
 
Cybersecurity Challenges with Generative AI - for Good and Bad
Cybersecurity Challenges with Generative AI - for Good and BadCybersecurity Challenges with Generative AI - for Good and Bad
Cybersecurity Challenges with Generative AI - for Good and BadIvo Andreev
 
20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.
20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.
20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.Sharon Liu
 
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdfARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdfTobias Schneck
 

Kürzlich hochgeladen (20)

Salesforce AI Associate Certification.pptx
Salesforce AI Associate Certification.pptxSalesforce AI Associate Certification.pptx
Salesforce AI Associate Certification.pptx
 
Growing Oxen: channel operators and retries
Growing Oxen: channel operators and retriesGrowing Oxen: channel operators and retries
Growing Oxen: channel operators and retries
 
Enterprise Document Management System - Qualityze Inc
Enterprise Document Management System - Qualityze IncEnterprise Document Management System - Qualityze Inc
Enterprise Document Management System - Qualityze Inc
 
Leveraging DxSherpa's Generative AI Services to Unlock Human-Machine Harmony
Leveraging DxSherpa's Generative AI Services to Unlock Human-Machine HarmonyLeveraging DxSherpa's Generative AI Services to Unlock Human-Machine Harmony
Leveraging DxSherpa's Generative AI Services to Unlock Human-Machine Harmony
 
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
 
Your Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
Your Vision, Our Expertise: TECUNIQUE's Tailored Software TeamsYour Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
Your Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
 
Streamlining Your Application Builds with Cloud Native Buildpacks
Streamlining Your Application Builds  with Cloud Native BuildpacksStreamlining Your Application Builds  with Cloud Native Buildpacks
Streamlining Your Application Builds with Cloud Native Buildpacks
 
Fields in Java and Kotlin and what to expect.pptx
Fields in Java and Kotlin and what to expect.pptxFields in Java and Kotlin and what to expect.pptx
Fields in Java and Kotlin and what to expect.pptx
 
eAuditor Audits & Inspections - conduct field inspections
eAuditor Audits & Inspections - conduct field inspectionseAuditor Audits & Inspections - conduct field inspections
eAuditor Audits & Inspections - conduct field inspections
 
Watermarking in Source Code: Applications and Security Challenges
Watermarking in Source Code: Applications and Security ChallengesWatermarking in Source Code: Applications and Security Challenges
Watermarking in Source Code: Applications and Security Challenges
 
Deep Learning for Images with PyTorch - Datacamp
Deep Learning for Images with PyTorch - DatacampDeep Learning for Images with PyTorch - Datacamp
Deep Learning for Images with PyTorch - Datacamp
 
Introduction-to-Software-Development-Outsourcing.pptx
Introduction-to-Software-Development-Outsourcing.pptxIntroduction-to-Software-Development-Outsourcing.pptx
Introduction-to-Software-Development-Outsourcing.pptx
 
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/ML
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/MLBig Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/ML
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/ML
 
Generative AI for Cybersecurity - EC-Council
Generative AI for Cybersecurity - EC-CouncilGenerative AI for Cybersecurity - EC-Council
Generative AI for Cybersecurity - EC-Council
 
IA Generativa y Grafos de Neo4j: RAG time
IA Generativa y Grafos de Neo4j: RAG timeIA Generativa y Grafos de Neo4j: RAG time
IA Generativa y Grafos de Neo4j: RAG time
 
JS-Experts - Cybersecurity for Generative AI
JS-Experts - Cybersecurity for Generative AIJS-Experts - Cybersecurity for Generative AI
JS-Experts - Cybersecurity for Generative AI
 
online pdf editor software solutions.pdf
online pdf editor software solutions.pdfonline pdf editor software solutions.pdf
online pdf editor software solutions.pdf
 
Cybersecurity Challenges with Generative AI - for Good and Bad
Cybersecurity Challenges with Generative AI - for Good and BadCybersecurity Challenges with Generative AI - for Good and Bad
Cybersecurity Challenges with Generative AI - for Good and Bad
 
20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.
20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.
20240319 Car Simulator Plan.pptx . Plan for a JavaScript Car Driving Simulator.
 
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdfARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
 

Azure Machine Learning

  • 1. Azure Machine Learning (Data Camp) Mostafa Elzoghbi Sr. Technical Evangelist – Microsoft @MostafaElzoghbi http://mostafa.rocks
  • 2. Session Objectives & Takeaways • What is Data Science? • What is Machine Learning? • Why do we need Machine Learning? • Azure ML studio platform capabilities. • Machine Learning basic concepts & algorithms: • How to build a model • Supervised vs Unsupervised models • Evaluate a model. • ML algorithms: Regression, classification, clustering and recommendations.
  • 3. Data Science Involves • Data science is about using data to make decision that drive actions. • Data science process involves: • Data selection • Preprocessing • Transformation • Data Mining • Delivering value from data : Interpretation and evaluation
  • 4. Data Science • Data Science is far too complex • Cost of accessing/using efficient ML algorithms is high • Comprehensive knowledge required on different tools/platforms to develop a complete ML project • Difficult to put the developed solution into a scalable production stage • Need a simpler/scalable method: Azure Machine Learning Service
  • 5. What is Machine Learning ? • Using known data, develop a model to predict unknown data. Known Data: Big enough archive, previous observations, past data Model: Known data + Algorithms (ML algorithms) Unknown Data: Missing, Unseen, not existing, future data
  • 6. Microsoft & Machine Learning Bing maps launches What’s the best way to home? Kinect launches What does that motion “mean”? Azure Machine Learning GA 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
  • 8. Machine learning enables nearly every value proposition of web search.
  • 10. Accent Color: Which border color is the best?
  • 14. Text Analytics: User reviews Positive Negative
  • 15. Microsoft Azure Machine Learning Make machine learning accessible to every enterprise, data scientist, developer, information worker, consumer, and device anywhere in the world.
  • 16. From data to decisions and actions Value
  • 17. Transform data into intelligent action DATA Business apps Custom apps Sensors and devices INTELLIGENCE ACTION People Automated Systems
  • 18. Microsoft Azure Machine Learning • Web based UI accessible from different browsers • Share|collaborate to any other ML workspace • Drag & Drop visual design|development • Wide range of ML Algorithms catalog • Extend with OSS R|Python scripts • Share|Document with IPython|Jupyter • Deploy|Publish|Scale rapidly (APIs)
  • 19. Fully managed Integrated Flexible Deploy in minutes No software to install, no hardware tomanage, all you need is an Azure subscription. Drag, drop and connect interface. Data sources with just a drop down; run across any data. Built-in collection of best of breed algorithms with no coding required. Drop in custom R or use popular CRAN packages. Operationalize models as web services with a single click. Monetize in Machine Learning Marketplace.
  • 20. Azure Machine Learning Ecosystem Get/Prepare Data Build/Edit Experiment Create/Update Model Evaluate Model Results Publish Web Service Build ML Model Deploy as Web ServiceProvision Workspace Get Azure Subscription Create Workspace Publish an App Azure Data Marketplace
  • 21. Blobs and Tables Hadoop (HDInsight) Relational DB (Azure SQL DB) Data Clients Model is now a web service that is callable Monetize the API through our marketplace API Integrated development environment for Machine Learning ML STUDIO
  • 24. 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.
  • 25. Model (Regression) 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
  • 27. 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
  • 29. Classify a news article as (politics, sports, technology, health, …) Politics Sports Tech Health Model (Classification) Using known data, develop a model to predict unknown data.
  • 30. Known data (Training data) 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 Process the raw data
  • 31. Known data (Training data) Using known data, develop a model to predict unknown data. LabelsDocuments Feature Documents Labels Tech Health Politics Politics Sports Process each data instance to represent it as a feature vector
  • 32. Feature vector Known data Data instance i.e. {40, (180, 82), (11,7), 70, …..} : Healthy Age Height/Weight Blood Pressure Hearth Rate LabelFeatures Feature Vector
  • 33. Developing a Model 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
  • 34. Model’s Performance 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 +/- +/- +/-
  • 35. Steps to Build a Machine Learning Solution 1 Problem Framing 2 Get/Prepare Data 3 Develop Model 4 Deploy Model 5 Evaluate / Track Performance 3.1 Analysis/ Metric definition 3.2 Feature Engineering 3.3 Model Training 3.4 Parameter Tuning 3.5 Evaluation
  • 37. Machine Learning Algorithms • ML Algorithm defines how your model will react • Which Algorithm to use? Depends on: • Data Quality • Data Size • What you want to predict • Time constraint • Computation power • Memory limits
  • 38. Machine Learning Algorithms You can develop solutions by using • Custom algorithms written in R | Python • Ready to use ML services from data market • Existing algorithms
  • 39. Machine Learning Algorithms Two major category of algorithms • Supervised • Unsupervised Most commonly used machine learning algorithms are supervised (requires labels) • Supervised learning examples • This customer will like coffee • This network traffic indicates a denial of service attack • Unsupervised learning examples • These customers are similar • This network traffic is unusual
  • 40. Common Classes of Algorithms (Supervised|Unsupervised) Classification Regression Anomaly Detection Clustering Supervised Supervised SupervisedUnSupervised
  • 41. Why you need to know these algorithms? • If you want to answer a YES|NO question, it is classification • If you want to predict a numerical value, it is regression • If you want to group data into similar observations, it is clustering • If you want to recommend an item, it is recommender system • If you want to find anomalies in a group, it is anomaly detection and many other ML algorithms for specific problem
  • 42. Classification Scenarios:  Which customer are more likely to buy, stay, leave (churn analysis)  Which transactions|actions are fraudulent  Which quotes are more likely to become orders  Recognition of patterns: speech, speaker, image, movement, etc. Algorithms: Boosted Decision Tree, Decision Forest, Decision Jungle, Logistic Regression, SVM, ANN, etc. Classification
  • 43. Clustering Scenarios:  Customer segmentation: divide a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests, spending habits, etc.  Market segmentation  Quantization of all sorts, such as, data compression, color reduction, etc.  Pattern recognition Algorithms: K-means Clustering
  • 44. Regression Scenarios:  Stock prices prediction  Sales forecasts  Premiums on insurance based on different factors  Quality control: number of complaints over time based on product specs, utilization, etc.  Workforce prediction  Workload prediction Algorithms: Bayesian Linear, Linear Regression, Ordinal Regression, ANN, Boosted Decision Tree, Decision Forest Regression
  • 45. Regression versus Classification Does your customer want to predict|estimate a number (regression) or apply a label|categorize (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
  • 46. Binary versus Multiclass Classification Does your customer want a yes|no answer? • Binary examples • click prediction • yes|no • over|under • win|loss • Multiclass examples • kind of tree • kind of network attack • type of heart disease
  • 48. References • Free e-book “Azure Machine Learning” • https://mva.microsoft.com/ebooks#9780735698178 • Azure Machine Learning documentation • https://azure.microsoft.com/en-us/documentation/services/machine- learning/ • Data Science and Machine Learning Essentials • www.edx.org • Azure ML HOL (GitHub): • https://github.com/Azure-Readiness/hol-azure-machine-learning/
  • 49. HOL Document • Access Azure HOL Doc: https://aka.ms/azuremlhol
  • 50. Thank you • Check out my blog for Azure ML articles: http://mostafa.rocks • Follow me on Twitter: @MostafaElzoghbi