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Machine learning isn’t only in the cloud. Microsoft is bringing it to PCs in the next Windows 10 release. Here’s how to get started now.
We’re not far away from a new release of Windows 10, and with it plenty of new APIs for your applications. One big change is support for running trained machine learning models as part of Windows applications, taking advantage of local GPUs to accelerate machine learning applications.
Building a machine learning application can be a complex process. Training a model can require a lot of data, and a considerable amount of processing power. That’s fine if you’ve got access to a cloud platform and lots of bandwidth, but what if you want to take an existing model from GitHub and run it on a PC?
Trained machine learning models are an ideal tool for bringing the benefits of neural networks and deep learning to your applications. All you should need to do is hook up the appropriate interfaces, and they should run as part of your code. But with many machine learning frameworks and platforms, there’s a need for a common runtime that can use any of the models out there. That’s where the new Windows machine learning tools come into play, offering Windows developers a platform to run existing machine learning models in their applications, taking advantage of a developing open standard for exchanging machine learning models. Continue reading
Recognize how software lifecycles can be used to build good practices for IT infrastructure managers. Appreciate how lifecycle support processes can improve the quality of IT services delivered to customers.
The closer the dialog is between the creative team and the delivery team, the better the end result. That’s why it’s important that software developers and IT infrastructure managers communicate and coordinate when planning IT service delivery. The focus should be on ensuring that captured information is processed so that it aligns with required business information outcomes.
We’re not saying that infrastructure managers should take over responsibility for selecting software lifecycles for development work, and we’re not advocating interference. Quite the opposite: Software developers must retain responsibility for software lifecycle selection and associated process modeling.
What we are recommending is that key personnel (IT service customers and IT infrastructure managers) should participate in a coordinated approach to the service planning processes, and that software developers should seek the input of IT service customers and IT infrastructure managers. Continue reading