The Machine Learning Building Blocks Developers Require to d

It is becoming increasingly apparent that enterprises must, in the near future, integrate machine learning (ML) in their software development pipelines in order to remain competitive in some way. But what exactly does AI and ML integration mean for any organization, whether a small-to-medium-sized company or a large multinational or a government agency with small offices?
In the recently recorded “Advanced Developer Workloads with Built-In AI Acceleration” livestream broadcast, Intel’s AI expert Jordan Plawner, director of products and business strategy, artificial intelligence, described the building blocks DevOps teams require to harness the power of AI and ML for application development and deployments. Hosted by Alex Williams, founder and publisher of The New Stack, Plawner was able to draw upon his experience to explain how data scientists and DevOps teams can take advantage of ML. Since his main role is to ensure Intels’ Xeon server processors and software are built to spec for DevOps teams’ workloads and library needs, Plawner is able to draw on his deep understanding of MLOps hardware and supporting software requirements.