Automating MLOps, DevOps, and DataOps for Data Scientists and ML Teams

[maltiger]

The unprecedented promise of machine learning (ML) is still unrealized, because data scientists are spending most of their time on non-data-science work. The common practice is that ML development through deployment relies on ad hoc tools, plug-ins, scripts, and a myriad of siloed tools that are impeding organizations, large and small, from streamlining ML development. […]

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https://blog.netapp.com/data-science-pipeline-solution