Interest in AI / ML is exploding, but these new techniques and technologies present some unique challenges that can result in suboptimal results if not addressed correctly. Dysfunctional AI / ML efforts can be characterized by high costs, an inability to scale, and slow or unnecessarily limited outcomes -- but it doesn't have to be that way. In a recent webinar, MLOps in Action: Real-World Examples for Establishing Best Practices, the Maven Wave / Atos team delivered a comprehensive look at how to diagnose problems and improve on the AI / ML efforts by focusing on ten facets in an MLOps assessment. During the discussion, six takeaways emerged that illuminate what to expect from an MLOps approach and how to best proceed. A common problem with any new technology is the wishful thinking that it will be a panacea for whatever challenges the enterprise faces.

There has been a lot written about the transformational power of artificial intelligence. If you are a regular reader of this column, you have gained the perspectives of eight of the leading thinkers on the topic. Nick Bostrom is perhaps the most influential thinker on safety concerns associated with the march toward artificial intelligence. He calls artificial intelligence "the single most important and daunting challenge that humanity has ever faced." Bostrom is an extraordinary polymath, having earned degrees in physics, philosophy, mathematical logic, and neuroscience.