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.

To democratise the use of machine learning, Google has condensed their years of research into a paper titled "A Rubric for ML Production Readiness" …

Inside IT organizations, getting machine learning technologies from pilot into production is one of the hot topics of 2019. At this point, many organizations have run successful pilots. Yet many more have still haven't achieved the value promised by machine learning because it isn't integrated into organizational processes. A Gartner study showed that only 47% of machine learning models are making it into production. Organizations are employing a few different methods to get their machine learning investments to production.