The new category is often called MLOps. While there isn’t an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: By adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too.
Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software.