Misha Bilenko @ Microsoft

1. http://azure.com/ml

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5. >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//] >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//]

6. >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//] >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//]
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7. >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//] >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//]

9. >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//] >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//]
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11. >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//] >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//]
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13. Mark Ready for Production
Ready
For Production
Sent to
Deploy
Published
To Marketplace

14. >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//] >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//]
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17. Save
// Call the Marketplace apito get the Frequently Bought Together products for this product
varprediction = GetJsonObject("https://api.datamarket.azure.com/ data.ashx/ama/mba-preview/v1/GetItemSetForItem?key='demo'&item='" + Model.Id+ "'","AccountKey", "Q6pX0DpKFsoFSgbxstlgzo1wtmCMQhr0Kf4rky MuTVQ");
// Get the product information from the Database
varboughtTogether= (prediction != null) ? GetProducts((object[]) prediction.ItemSet) : newList<ProductOverviewModel>();
}
<!--Apply HTML template for the Frequently Bought Together Products -->
@Html.Partial("FrequentlyBoughtTogether",boughtTogether)

18. >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//] >>//[Aeoriuapakfajoerauiepraup]<</ /4348957t894qfo<<//[http://insideapps.//]//>>p//color{//yellow//]

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-Product: Introducing Azure ML
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21. http://azure.com/ml
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23. Develop Model
Deploy Model

24. Classification
Regression
Recommenders
Clustering
•State of art ML Algorithms

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Dataset 1, called as:
maml.mapInputPort(1)
Dataset 2, called as:
maml.mapInputPort(2)
Contents of optional Zip port are in ./src/, called as:
source("src/yourfile.R");
load("src/yourData.rdata");
Output data.frame, called as:
maml.mapOutputPort
R Device output –standard output and graphics

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

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-Backend:Many-{task,algo,platform} ML toolbox
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- publicinterfaceIPredictor<inTFeatures, outTResult> publicinterfaceITrainer<inTDataSet, outTPredictor> whereTPredictor: IPredictor

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-Language: Net# for Deep Neural Net Topologies

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34. Convolutional
1 hidden layer, fully-connected
Input: 28x28
Hidden: 50
Output: 10
2 hidden layers, fully-connected
Input: 28x28
Hidden: 100
Output: 10
Hidden: 200
28
28
5
5
Conv1
Image
24
24
2
2
Pool1
12
12
8
Conv2
8
2
2
Pool2
4
4
5
5
32
Hidden: 120
Output: 10
32

35. 
inputImage [28, 28];
hiddenH1 [200] fromImage all;
hiddenH2 [100] fromH1 all;
outputOutput[10] softmaxfromH2 all;
Image: 28x28
H1: 200
H2: 100
Output: 10

36. const{kernelSize1 = 5; mapCount1 = 5}
inputImage [28, 28];
hiddenConv1 [mapCount1,24,24] fromImage convolve {
KernelShape= [kernelSize1, kernelSize1];
MapCount= mapCount1;
InputShape= [28, 28];
};
hiddenPool1 [5, 12, 12] fromConv1 max pool {
KernelShape= [1, 2, 2];
Stride = [1, 2, 2];
InputShape= [mapCount1, 24, 24];
};
...
hiddenHidden[120] fromPool2 all;
outputOutput[10] softmaxfromH2 all;
28
28
5
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Conv1
Image
24
24
2
2
Pool1
12
12
8
Conv2
8
2
2
Pool2
4
4
5
5
32
Hidden: 120
Output: 10
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•Azure ML Net#

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-Backend:Many-{task,algo,platform} ML toolbox
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39. -http://azure.com/ml
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http://aka.ms/azuremlawardskenji.takeda@microsoft.com