Build custom models with Azure Machine Learning Designer

Machine learning is an important part of modern application development, replacing much of what used to be done using a complex series of rules engines, and expanding coverage to a much wider set of problems. Services like Azure’s Cognitive Services provide prebuilt, pretrained models that support many common use cases, but many more need custom model development.

Going custom with ML

How do we go about building custom machine learning models? You can start at one end using statistical analysis languages like R to build and validate models, where you’ve already got a feel for the underlying structure of your data, or you can work with the linear algebra features of Python’s Anaconda suite. Similarly, tools such as PyTorch and TensorFlow can help construct more complex models, taking advantage of neural nets and deep learning while still integrating with familiar languages and platforms.

That’s all good if you’ve got a team of data scientists and mathematicians able to build, test, and (most importantly) validate their models. With machine learning expertise hard to find, what’s needed are tools to help guide developers through the process of creating the models that businesses need. In practice, most machine learning models fall into two types: the first identifies similar data, the second identifies outlying data.

We might use the first type of app to identify specific items on a conveyor belt or have the second look for issues in data from a series of industrial sensors. Scenarios like these aren’t particularly complex, but they still require building a validated model, ensuring that it can identify what you’re looking for and find the signal in the data, not amplify assumptions or respond to noise.

Introducing Azure Machine Learning Designer

Azure provides various tools for this, alongside its prebuilt, pretrained, customizable models. One, Azure Machine Learning Designer, lets you work with your existing data with a set of visual design tools and drag-and-drop controls.

You don’t need to write code to build your model, though there’s the option to bring in custom R or Python where necessary. It’s a replacement for the original ML Studio tool, adding deeper integration into Azure’s machine learning SDKs and with support for more than CPU-based models, offering GPU-powered machine learning and automated model training and tuning.

Copyright © 2020 IDG Communications, Inc.

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