The Neuromation team has just published a new report on the state of Machine Learning Operations Platforms in 2021. MLOps was defined as a separate discipline only recently when the ML practitioners moved from university labs to corporate boardrooms. AI and ML leaders today already have a better understanding of the MLOps lifecycle and the procedures and technology required for deploying new models into production and subsequently scaling them. The young MLOps market already has a host of established players as well as newer niche entrants.
This report placed 10 MLOPs vendors under a microscope to assess how they stack up in terms of infrastructure, data management, model development experience, model training, and management and deployment capabilities. The attempt was to coax clarity out of chaos across MLOps platform functionality, positioning, and the ability to deliver in demanding environments.
In assembling this report, it was seen again and again that interoperability is one of data scientists’ critical requirements for MLOps Platforms. Interoperability refers to the ease of integrating various MLOps tools into a single consolidated pipeline for end-to-end operations.
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