Associative memory is a strategy developed by living organisms over many years of evolution. This is true of humans, as we use associative memory to tell a story about two unrelated things to grasp more information in less time. Memory optimisation is important in case of resource-hungry deep learning tasks as well. Though the analogy between neuroscience and artificial neural networks has been beaten to a pulp, mimicking nature is still opening up exciting avenues in the world of AI. Memory association in machine learning is of great significance.

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.