AI applications typically require massive volumes of data and multiple devices within the datacenter
Tag: @ChrisGrundemann
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
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
Ken Grohe of Weka discusses various business use cases for AI-enabled applications with Chris Grundemann and Stephen Foskett
AI will be part of everything we do in the future, not replacing us but augmenting our work, and this is especially true in information security
Just as data analytics transformed business intelligence so is artificial intelligence transforming data science
In this episode, we ask Red Hat about the platform requirements for AI applications in production