Conversational artificial intelligence (AI) is enabling organizations to improve their business in areas like customer service and employee engagement by automating some of the most commonly requested services, which frees up employees to take on more value-adding activities. While the benefits of conversational AI are well established, determining who in an organization should build these solutions is not always clear.
In an age where low-latency and data security can be the lifeblood of a business, containers make it possible for enterprises to meet these needs when harnessing artificial intelligence (AI). Since introducing Azure Cognitive Services in containers this time last year, businesses across industries have unlocked new productivity gains and insights.
Multi-language speech transcription was recently introduced into Microsoft Video Indexer at the International Broadcasters Conference (IBC). It is available as a preview capability and customers can already start experiencing it in our portal.
Organizations face challenges when it comes to extracting insights, finding meaning, and uncovering new opportunities in the vast troves of content at their disposal. In fact, 82 percent of…
It's exciting to see the PyTorch Community continue to grow and regularly release updated versions of PyTorch! Recent releases improve performance, ONNX export, TorchScript, C++ frontend, JIT, and distributed training. Several new experimental features, such as quantization, have also been introduced.
This week at Microsoft Ignite, we announced updates to our products to make it easier for organizations to build robust conversational solutions, and to deploy them wherever their customers are. We are sharing some of the highlights below.
Azure Cognitive Services brings AI within reach of every developer without requiring machine learning expertise. All it takes is an API call to embed the ability to see, hear, speak, understand, and accelerate decision-making into your apps.
Over the past few years, we’ve seen incredible transformation across industries as companies harness the power of AI to transform business processes and drive impact for their customers.
Congratulations to the TensorFlow community on the release of TensorFlow 2.0! In this blog, we aim to highlight some of the ways that Azure can streamline the building, training, and deployment of your TensorFlow model.
Azure Machine Learning is the center for all things machine learning on Azure, be it creating new models, deploying models, managing a model repository and/or automating the entire CI/CD pipeline for machine learning. We recently made some amazing announcements on Azure Machine Learning, and in this post, I’m taking a closer look at two of the most compelling capabilities that your business should consider while choosing the machine learning platform.