MLOps is an engineering discipline that aims to unify ML systems development (dev) and ML systems deployment (ops) in order to standardize and streamline the continuous delivery of high-performing models in production.
In order to understand MLOps, we must first understand the ML systems lifecycle. The lifecycle involves several different teams of a data-driven organization.
From start to bottom, the following teams chip in:
- Business development or Product team — defining business objective(s) with KPIs
- Data Engineering — data acquisition and preparation.
- Data Science — architecting ML solutions and developing models.
- IT or DevOps — complete deployment setup, monitoring alongside scientists.