Microsoft Developer Day

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Charla Ellis
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1 Microsoft Developer Day

2 Ujjwal Kumar Microsoft Developer Day Senior Technical Evangelist, Microsoft

3 Senior Technical Evangelist

4 Agenda Microsoft Developer Day

5 Microsoft Azure Machine Learning

6 What is Machine Learning (ML) Using known data, develop a model to predict unknown data Computing Systems that become smarter with Experience Experience = Past Data + Human Input

7 What is Machine Learning (ML)

8 Microsoft & Machine Learning Hotmail launches Which is junk? Bing maps launches What s the best way to home? Bing search launches Which searches are most relevant? Kinect launches What does that motion mean? Skype Translator launches What is that person saying? Azure Machine Learning GA Cortana Intelligence Microsoft Cognitive Bot Framework What will happen next?

9 Why Now? + + =

10 Why Machine Learning?

11 Machine learning enables nearly every value proposition of web search.

12 Image Analyze

13 Accent Color Which border color is the best?

14 Analyze Image- Accent Color

15 Accent color in Windows 10 Store

16 Text Analytics User reviews Positive Negative

to develop a complete ML project Difficult to put the developed solution into a

17 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

18 Microsoft Azure Machine Learning Make machine learning accessible to every enterprise, data scientist, developer, information worker, consumer, and device anywhere in the world.

19 From data to decisions and actions Value

20 Cortana Intelligence- Transform data into intelligent action Business apps People Custom apps Sensors and devices Automated Systems DATA INTELLIGENCE ACTION

Microsoft Azure Machine Learning Web based UI accessible from different browsers Share/collaborate to any other ML workspace Drag&Drop visual

21 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)

22 Microsoft Azure Machine Learning ML Algorithms ML Studio ML Operationalization ML APIs Marketplace Best of MS Data Scientist IT Professional ISVs & Developers

23 Azure Machine Learning Ecosystem Provision Workspace Build ML Model Deploy as Web Service Publish an App Get/Prepare Data Get Azure Subscription Create Workspace Evaluate Model Results Build/Edit Experiment Publish Web Service Azure Data Marketplace Create/Update Model

Green Score, Wealth Score, Giving Score Frequently Bought Together API Recommendations API Anomaly Detection API Lexicon Based Sentiment Analysis Forecasting-Exponential Smoothing Forecasting -

24 Green Score, Wealth Score, Giving Score Frequently Bought Together API Recommendations API Anomaly Detection API Lexicon Based Sentiment Analysis Forecasting-Exponential Smoothing Forecasting - ETS+STL Forecasting-AutoRegressive Integrated Moving Average (ARIMA) Binary Classifier API Cluster Model API Survival Analysis API Multivariate Linear Regression API Survival Analysis API Multivariate Linear Regression API Normal Distribution Quantile Calculator Binomial Distribution Quantile Calculator And more on datamarket.azure.com

25 What is Azure Machine Learning ML STUDIO API

26 Example use cases

27 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

28 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

29 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

30 Common Classes of Algorithms (Supervised Unsupervised) Classification Clustering Regression Anomaly Detection

31 Why 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

32 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. Classification Algorithms: Boosted Decision Tree, Decision Forest, Decision Jungle, Logistic Regression, SVM, ANN, etc.

33 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 Clustering Algorithms: K-means

34 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 Regression Algorithms: Bayesian Linear, Linear Regression, Ordinal Regression, ANN, Boosted Decision Tree, Decision Forest

Regression versus Classification Does your customer want to predict/estimate a number (regression) or apply a label/categorize (classification)?

35 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

36 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

37 Azure Machine Learning Classifier Cheat Sheet:

38 Azure ML Cheat Sheet

39 Azure Machine Learning Studio Car price prediction DEMO 39

40 Announced at //Build - #ProjectOxford

42 Microsoft Cognitive APIs

43 DEMO AND HANDS ON Microsoft Cognitive APIs & Bot Framework demo and hands on 43

45 Next Steps Explore the other Data Services available on Azure Find Azure ML Tutorials Available in the Gallery Use Azure ML to Predict a Scenario Relevant to you!

46 References Related references for you to expand your knowledge on the subject Hands on Lab(s) Azure Portal Azure Updates Microsoft Virtual Academy aka.ms/mva Developer Network msdn.microsoft.com/

Thanks for your time Ujjwal Kumar @ujjwalkr Useful Links: http://azure.microsoft.

47 Thanks for your time Ujjwal Useful Links: - sign up for your trial - log into the studio - check out the gallery

48 Thank you Follow us online Microsoft Developer Day

Being Smarter with Azure Machine Learning and R

Being Smarter with Azure Machine Learning and R Leila Etaati SQL Saturday #464, Melbourne 20 th February 2016 Housekeeping Mobile Phones please set to stun during sessions Evaluations complete online to