Top 7 Checkpoints To Consider During Machine Learning Production

A major challenge for any company that is starting out in the realm of data-driven markets is the deployment of machine learning pipelines at full scale for their products. To tap the most out of AI, it is necessary to build service-specific tools and frameworks in addition to the existing models. The best strategy varies from product to product; but the rubrics of machine learning stay the same. To democratise the use of machine learning, Google has condensed their years of research into a paper titled "A Rubric for ML Production Readiness", where they listed out their findings in the form of 28 specific tests that have shown promising results. The offline/online metric relationship can be measured in one or more small scale A/B experiments using an intentionally degraded model.