Vertical first, horizontal second. How MLOps facilitates efficient vertical prototyping of Machine Learning systems.

Failure is inherent in doing data science. However, we have some options to reduce the blast radius of failure. Besides structuring the product development process appropriately, making the vertical breakthrough happen fast is key to detecting risks early and being able to iterate on the model development as close to a production setting and to the stakeholders as possible. Of course, developing vertical prototypes early can be expensive if your model development teams always need to take care of all the infrastructure, deployment and serving overhead. This is why the MLOps paradigm and, at its core, the establishment of a golden path — a set of processes, tools, templates and training — is crucial. It enables model development teams to push out vertical prototypes fast at very little additional costs by reducing the non-model specific overhead that comes with bringing ML systems (close) to production, eventually reducing the blast radius of failure in data science.