Spell Bringing MLOps to Deep Learning to Ease the Deep Learning Path for Enterprises

Making machine learning operations easier to use, manage and organize for enterprises has always been the goal of the series of best practices known as MLOps. But while MLOps works well for the needed processes and commodity CPU-based infrastructure of traditional machine learning, it can come up short in being as useful for more complex deep learning workloads, which can be far larger and more demanding than traditional machine learning requirements. To fill this gap, New York-based startup, Spell, has launched what it calls a cloud-agnostic MLOps platform that is targeted to serve the more complex and unique needs of deep learning using the principles of MLOps used for machine learning. "With deep learning, there aren't a lot of options, because people are using their own tools," Tim Negris, the head of marketing for Spell, told EnterpriseAI. But using the company's newly-unveiled platform, enterprises can now more easily manage their deep learning model training, orchestration, monitoring, reporting, dashboarding and more, he said.