Data scientists strive to build their models, their efforts are often complicated by a lack of alignment between rapidly evolving tools, affecting productivity and collaboration among their teams, software developers, and IT operations. On-premise resource limitations can limit scalability, such as quickly provisioning hardware. Popular cloud platforms offer the desired scale and attractive tools, but often lock users in, limiting architectural and deployment choices. With this solution, data scientists and developers can rapidly develop, train, test, and iterate ML and DL models in a fully supported sandbox environment—without waiting for infrastructure provisioning.
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