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 applications have large data volumes with lots of clients and conventional storage systems aren’t a good fit
AI and analytics needs access to massive volumes of data, but we are constantly reminded of the importance of securing data
Most organizations have a vast amount of so-called unstructured data, and this poses a major risk for operations
Big data really wasn’t all that big until modern analytics and machine learning applications appeared, but now storage solutions have to scale capacity and performance like never before
When it comes to AI, it’s garbage in, garbage out: A model is only as good as the data used