Hello everyone, As you know DLab is currently incubating. The podling positions DLab as an exploratory, r&d environment where data scientists can create and train models. In order for the model to be productionalized, additional steps needs need to be made to build and deploy such ML models to production environments at runtime. This major functionality is not yet part of DLab. During DLab incubation at Apache, EPAM developers have been implementing a tool, which significantly extends DLab with automated model deployment capabilities, including, but not limited to model performance monitoring, feedback loop, lifecycle management. This will bring DLab to a new level – a powerful self-service AI platform for end-to-end lifecycle management of ML models and environments for them. We’d be happy to extend DLab with such capabilities and share it with community. Dear mentors, could you please assist us, defining next steps for podling to proceed and being able to smoothly integrate/extend DLab with the tool described above within current DLab incubation process. Thanks in advance for your assistance.
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