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Extracting Information From Unstructured Documents with Azure Form Recognizer
Extracting information from unstructured documents such as contracts is usually manual and involves tediously reading and understanding substantial amounts of documents to find specific information and manually extracting the information to digitize it. The process consumes a significant amount of a lawyer’s billable hours and is... Read more
Creating Managed Online Endpoints in Azure ML
Suppose you’ve trained a machine learning model to accomplish some task, and you’d now like to provide that model’s inference capabilities as a service. Maybe you’re writing an application of your own that will rely on this service, or perhaps you want to make the service... Read more
Top 10 AI Companies Coming to the ODSC East AI Expo Hall
With ODSC East right around the corner, coming April 19-21, it’s time to focus on some of the hot topics that you need to know. On everyone’s mind of course is artificial intelligence, where it’s going, and what will the future look like as the scale... Read more
Creating Spectrograms and Scaleograms for Signal Classification
In this post, I’ll explain how to convert time-series signals into spectrograms and scaleograms, which are image representations of those signals that contain both frequency and time information. In a future post, we’ll use the images created here to classify the signals. I’ll explain the intuition... Read more
Emily Dickinson and the Meter of Mood: An Experiment in Text Analysis
Article by Jen Looper, Principal Cloud Developer Advocate Lead at Microsoft on the Next Generation Team. I tie my Hat — I crease my Shawl — Life’s little duties do — precisely — As the very least Were infinite — to me — I put new... Read more
Learn to Implement Responsible AI with Microsoft on Dec 7
As AI becomes integrated with many businesses, organizations, and nonprofits, new dilemmas arise beyond just productivity and efficiency. Responsible AI is paramount to successful AI implementation, ensuring that the algorithms are not just unbiased and objective, but represent the most accurate and current data possible. At... Read more
Scale Model Deployment Anywhere with Azure Arc Enabled Machine Learning
The Azure Machine Learning team is excited to announce the public preview of Azure Arc-enabled Machine Learning for inference. This builds on our training preview, enabling customers to deploy and serve models in any infrastructure on-premises and across multi-cloud using Kubernetes. With a simple AzureML extension... Read more
Since the beginning of the COVID pandemic, there have been more than 700000 scientific papers published on the subject. A human researcher cannot possibly get acquainted with such a huge text corpus — and therefore some help from AI is highly needed. In this post, we... Read more
Introduction to PyTorch
Recently, Microsoft and PyTorch announced a “PyTorch Fundamentals” tutorial, which you can find on Microsoft’s site and on PyTorch’s site. The code in this post is based on the code appearing in that tutorial, and forms the foundation for a series of other posts, where I’ll explore other... Read more
Model Performance Optimization with TorchServe
In this blog, we are going to take a look at TorchServe, a feature-rich framework for serving machine learning models. We will go through a variety of experiments to test the performance on different operating systems, thread settings, and a number of workers to discover the... Read more