Amazon SageMaker Named as the Outright Leader in Enterprise MLOps Platforms

Over the last few years, Machine Learning (ML) has proven its worth in helping organizations increase efficiency and foster innovation. As ML matures, the focus naturally shifts from experimentation to production. ML processes need to be streamlined, standardized, and automated to build, train, deploy, and manage models in a consistent and reliable way. Perennial IT concerns such as security, high availability, scaling, monitoring, and automation also become critical. Great ML models are not going to do much good if they can't serve fast and accurate predictions to business applications, 24/7 and at any scale.