Machine learning is changing how organizations work, enabling them to use massive datasets that help drive decision making and automate processes. However, the deployment, configuration, testing, and operation of ML are critical to ML success on an ongoing basis, requiring data scientists to attend to operational and administrative infrastructure details and sapping the time that they would otherwise spend applying their expertise. DevOps processes have helped optimize the delivery of traditional IT workloads and services, and a similar set of methodologies, tailored for the life cycle and resource needs of ML, are needed to get the most out of the data.
This new set of methodologies—MLOps—is the approach to the deployment and management of ML solutions that can drive better ROI from ML and AI by allowing data scientists to focus more on model development, experimentation, and training and less on operational concerns. This focus empowers the organization to get ML and AI solutions out of the lab and into production much faster, driving more insights, better decision-making, and quicker, more assured achievement of goals. While roughly half of the ML projects never leave the lab, Dell Technologies and its partners are targeting this gap by delivering the cnvrg.io MLOps deployment and management solution that enables organizations to truly embrace ML and deliver far greater value.