6 Crucial Considerations for MLOps Success

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.