Fig. 1: MLTRL spans research (red) through prototyping (orange), productization (yellow), and deployment (green).
From: Technology readiness levels for machine learning systems
Most ML workflows prescribe an isolated, linear process of data processing, training, testing, and serving a model37. Those workflows fail to define how ML development must iterate over that basic process to become more mature and robust, and how to integrate with a much larger system of software, hardware, data, and people. Not to mention MLTRL continues beyond deployment: monitoring and feedback cycles are important for continuous reliability and improvement over the product lifetime.
