Conclusion
As machine learning transitions from a research discipline to an applied field, we’ve seen a
yearly growth of 25 percent in ML pipeline development, deployment, and operation in various
industries. The business value of ML is realized through day-to-day ML operations and pipelines,
which, in turn, drive the research and development of ML models and algorithms. Nonetheless,
deploying ML in production presents numerous challenges, because it interweaves significantly
different activities and artifacts, such as data management, processing, analysis, modeling,
verification, and security. Through numerous AI/ML engagements with AWS customers, our Data
Science team has observed that a key challenge is the lack of an end-to-end workflow that would
provide a set of templates for optimally fusing or separating different ML DevOps activities and
artifacts. In this guide, we presented the ML Max
workflow
