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This manual synchronization process, hindered by disparate data formats, is resource-intensive, limiting the potential for widespread data orchestration. The platform, although functional, deals with CSV and JSON files containing hundreds of thousands of rows from various manufacturers, demanding substantial effort for dataingestion.
Data Estate: This element represents the organizational data estate, potential data sources, and targets for a data science project. DataEngineers would be the primary owners of this element of the MLOps v2 lifecycle. The Azure dataplatforms in this diagram are neither exhaustive nor prescriptive.
Automation You want the ML models to keep running in a healthy state without the data scientists incurring much overhead in moving them across the different lifecycle phases. It would make sure that all development and deployment workflows use good softwareengineering practices. My Story DevOps Engineers Who they are?
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