Developers of AI applications face many obstacles, but the chief challenge is simply that these are different from traditional software development projects
Tag: @ChrisGrundemann
Microsoft plays a large role in enterprise IT applications, from the desktop to the datacenter to the Azure cloud, and the company is active in the world of AI as well
Machine learning models have grown tremendously in recent years, with some having hundreds of billions of data points, and we wonder how big they can get
AI processing is appearing everywhere, running on just about any kind of infrastructure, from the cloud to the edge to end-user devices
Development of autonomous vehicles is an excellent example of machine learning applied to industrial IoT
We are on the cusp of a totally new architecture for enterprise IT, and this change toward composability is being driven by applications like AI
Industrial cameras and sensors are generating more data than ever, and companies are increasingly moving machine learning to the edge to meet it
AI applications typically require massive volumes of data and multiple devices within the datacenter
Training and optimizing a machine learning model takes a lot of compute resources, but what if we used ML to optimize ML? Luis Ceze created Apache Tensor Virtual Machine (TVM) to optimize ML models and has now founded a company, OctoML, to leverage this technology
AI and analytics needs access to massive volumes of data, but we are constantly reminded of the importance of securing data