Making the machine: the machine learning lifecycle Google Cloud Blog

The machine learning lifecycle consists of three major phases: Planning (red), Data Engineering (blue) and Modeling (yellow). In contrast to a static algorithm coded by a software developer, an ML model is an algorithm that is learned and dynamically updated. You can think of a software application as an amalgamation of algorithms, defined by design patterns and coded by software engineers, that perform planned tasks. Once an application is released to production, it may not perform as planned, prompting developers to rethink, redesign, and rewrite it (continuous integration/continuous delivery). We are entering an era of replacing some of these static algorithms with ML models, which are essentially dynamic algorithms.