- [Instructor] To send data to our custom model service, … we're going to create an anonymous object to contain it. … The structure of the object … can be found in the same place we got our URL from. … So we're going to go back to Azure Machine Learning Studio … and click on the request response. … And if we scroll down, … we can find information on what … the sample request should look like. … So we can see the column names, … and we see that we pass in an array of values. … So technically we could set up multiple ranges of values … to get predictions at the same time. … We're only going to do one at a time. … So now we're going to go back to Visual Studio, … and in our method we want to continue by making … an anonymous object for our request … using the structure that we just looked at … from the API documentation. … So we're going to go var, scoreRequest, … equals, and new, and this is going to be an object, … so it's going to be Inputs, … and that's going to equal a new object as well, …
- Defining machine learning
- Training a machine learning model
- Comparing machine learning frameworks
- Using IBM Watson for mobile machine learning
- Using Azure Machine Learning for speech and image recognition
- Training Core ML models
- Comparing client-side and server-side models
Skill Level Beginner
Xamarin and Android Studio: Material Designwith Kevin Ford1h 47m Intermediate
Machine Learning for iOS Developerswith Brian Advent1h 25m Advanced
1. Introduction to Machine Learning
2. Server Models: IBM Watson
3. Server Models: Azure Machine Learning
4. Client Models: Core ML
5. Understanding the Offerings
Next steps1m 40s
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