Machine learning models require DevOps-style workflows Posted on : Jan 15 - 2018

Big data is driving the use of AI and machine learning. But teams must be swift to embrace DevOps and re-evaluate models, according to Wikibon's James Kobielus

As 2018 gets rolling, it appears that various aspects of big data are morphing into machine learning and AI. The changes that machine learning models bring to big data analytics are not readily apparent.

To sort through recent developments, including data science and DevOps, reporter Jack Vaughan caught up with James Kobielus, lead analyst for AI, data science, deep learning and application development, at SiliconAngle/Wikibon. He had just finished a serious round of predicting 2018 with colleagues when we knocked on the door.

AI was asleep for a few years. Was it just waiting for big data to come along?

James Kobielus: Well, AI has been around a while. And, very much, it had been rule-based expert systems at the core of it. That meant fixed rules that had to be written by some subject matter experts.

What's happened in the last 10 years is that AI in the broad sense -- both in research and in the commercialization of the technology -- has shifted away from fixed, declarative, rule-based systems toward statistical, probabilistic, data-driven systems.

That is what machine learning models are about. Machine learning is the core of modern AI. It's all about using algorithms to infer correlations and patterns in data sets. That's for doing things like predictive analysis, speech recognition and so forth.

Much of the excitement more recently has been from neural networks -- statistical algorithms that in many ways are built to emulate the neural interconnections in our brains. Those too have been around since the 1950s, with a research focus. View More