http://azure.com/ml

--

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-

>>//[Aeoriuapakfajoerauiepraup]//[Aeoriuapakfajoerauiepraup]

>>//[Aeoriuapakfajoerauiepraup]//[Aeoriuapakfajoerauiepraup]//[Aeoriuapakfajoerauiepraup]//[Aeoriuapakfajoerauiepraup]

>>//[Aeoriuapakfajoerauiepraup]//[Aeoriuapakfajoerauiepraup]

>>//[Aeoriuapakfajoerauiepraup]//[Aeoriuapakfajoerauiepraup]

>>//[Aeoriuapakfajoerauiepraup]//[Aeoriuapakfajoerauiepraup]

Mark Ready for Production

Ready

For Production

Sent to

Deploy

Published

To Marketplace

>>//[Aeoriuapakfajoerauiepraup]//[Aeoriuapakfajoerauiepraup]

Save

// Call the Marketplace api to get the Frequently Bought Together products for this product

var prediction = 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 Databasevar boughtTogether = (prediction != null) ? GetProducts((object[]) prediction.ItemSet) : new List();

}

@Html.Partial("FrequentlyBoughtTogether", boughtTogether)

>>//[Aeoriuapakfajoerauiepraup]//[Aeoriuapakfajoerauiepraup]

-- Product: Introducing Azure ML

-

-

http://azure.com/ml

Develop Model Deploy Model

Classification Regression Recommenders Clustering

State of art ML Algorithms

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

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

--

- Backend: Many-{task,algo,platform} ML toolbox

-

--

-

-

-

-

--

-

-

-public interface IPredictor

public interface ITrainer

where TPredictor : IPredictor

--

-

-

--

-

-

--

-

- Language: Net# for Deep Neural Net Topologies

--

-

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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

Pool112

12

8Conv28

22

Pool24

4

5

5

32

Hidden: 120

Output: 10

32

input Image [28, 28];

hidden H1 [200] from Image all;

hidden H2 [100] from H1 all;

output Output [10] softmax from H2 all;

Image: 28x28

H1: 200

H2: 100

Output: 10

const {kernelSize1 = 5; mapCount1 = 5}

input Image [28, 28];

hidden Conv1 [mapCount1,24,24] from Image convolve {KernelShape = [kernelSize1, kernelSize1]; MapCount = mapCount1; InputShape = [28, 28];

};

hidden Pool1 [5, 12, 12] from Conv1 max pool {KernelShape = [1, 2, 2]; Stride = [1, 2, 2]; InputShape = [mapCount1, 24, 24];

};

...

hidden Hidden [120] from Pool2 all;

output Output [10] softmax from H2 all;

28

28

5

5

Conv1

Image

24

242 2

Pool11212

8

Conv28

2

2

Pool24

4

5

5

32

Hidden: 120

Output: 10

32

Azure ML Net#

--

- Backend: Many-{task,algo,platform} ML toolbox

-

- http://azure.com/ml

-

-

http://aka.ms/azuremlawards kenji.takeda@microsoft.com