Taming Machine Learning on AWS with MLOps: A Reference Architecture
Despite the investments and commitment from leadership, many organizations are yet to realize the full potential of artificial intelligence (AI) and machine learning (ML).
Data science and analytics teams are often squeezed between increasing business expectations and sandbox environments evolving into complex solutions. This makes it challenging to transform data into solid answers for stakeholders consistently.
How can teams tame complexity and live up to the expectations placed on them? Take this brief survey and learn how MLOps provides some answers.