Showing results for Machine Learning - Developer Support

Joseph Masengesho provides a step-by-step tutorial on how to deploy your first ML model on AKS. In a previous post, I provided a lengthy write-up about my understanding of using Kubernetes as a compute target in Azure ML from a Kubernetes architect’s perspective. In this post, I will offer a step-by-step tutorial that teaches you how to deploy...

Machine learning is a very powerful tool for businesses and researchers to create predictions for data problems. However, there are many steps to creating models and not every model is suited for each problem. We have to use a combination of human judgment and computational techniques to create the right model.

The most valuable lesson from our recent work was the time invested in identifying opportunities on how best to leverage AI to enhance our customer’s solutions before delving into architectural decisions and proceeding with a proof of concept.

Using multiple algorithms and tuning the algorithms to find the optimum value for each parameter also improves the accuracy of the model. However, it is not necessary that higher accuracy models always give the accurate results, as sometimes, the improvement in model’s accuracy can be due to over-fitting too.

Machine learning is the scientific study of algorithms and statistical models that result in devices automatically learning and improving from experiences without being explicitly programmed. With so much success integrating machine learning into our everyday lives, the obvious next step is to integrate machine learning into even more systems.

From the ready-to-consume set of Azure Cognitive Services to the comprehensive set of tools for data scientists available in Azure Machine Learning Service, there are many ways to apply AI into your products and services.

I find that machine learning experiment’s results are always interesting and somewhat unexpected in certain cases. On this comparison, the feature ranking results of PFI are often different from the feature selection statistics that are utilized before a model is created. This is useful in many cases, especially when training “black-box” models whe...

Microsoft’s vision is to democratize AI and make it accessible and valuable to everyone.  Join us to learn how to start building intelligence into your solutions with the Microsoft AI platform, including pre-trained AI services like Cognitive Services and Bot Framework, as well as deep learning tools like Azure Machine Learning.

In this post, App Dev Managers Edward Fry and Sheldon Ledbetter explorer the practical implications of Logical Regression and how we’re using to solve problems in systems via Machine Learning. Logistic Regression. The very phrase is a mouthful. It's easy to imagine it being used by actors to improve elocution or by math professors to punish ...

Senior Application Development Manager, Justin Scott, launched a new podcast this month you will want to check out.  AI exposed looks at the world of Artificial Intelligence-- discussing some of the latest trends, interviewing industry experts, and having fun in the process. In Episode#1, Justin Scott sits with one of our Microsoft data scie...