Download - Misha Bilenko @ Microsoft
http://azure.com/ml
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Mark Ready for Production
Ready
For Production
Sent to
Deploy
Published
To Marketplace
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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<ProductOverviewModel>();
}
<!-- Apply HTML template for the Frequently Bought Together Products -->@Html.Partial("FrequentlyBoughtTogether", boughtTogether)
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- Product: Introducing Azure ML
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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
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- Backend: Many-{task,algo,platform} ML toolbox
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-public interface IPredictor<in TFeatures, out TResult>
public interface ITrainer<in TDataSet, out TPredictor>
where TPredictor : IPredictor
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- 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
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• Azure ML Net#
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- Backend: Many-{task,algo,platform} ML toolbox
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