MLOps at the edge

Machine Learning Operations (MLOps) aims to integrate agile methodologies into the end-to-end process of running machine learning workloads. MLOps brings together best practices from data science, data engineering, and DevOps to streamline model design, development, and delivery across the machine learning development life cycle (MLDLC).

As per MLOps special interest group (SIG), MLOps is defined as "The extension of the DevOps methodology to include machine learning and data science assets as first-class citizens within the DevOps ecology." MLOps has gained rapid momentum in the last few years from ML practitioners and is a language-, framework-, platform-, and infrastructure-agnostic practice.

The following diagram shows the virtuous cycle of the MLDLC:

Figure 8.11 – MLOps workflow

The preceding diagram shows how Operations is a fundamental block of the ML workflow. We introduced some of the concepts of ML design...