Making the most of MLOps

When companies first start deploying artificial intelligence and building machine learning projects, the focus tends to be on theory. Is there a model that can provide the necessary results? How can it be built? How can it be trained? But the tools that data scientists use to create these proofs of concept often don't translate well into production systems.