Roadblocks to model production vary for different roles   🚧

From Anaconda, Inc. “The State of Data Science 2020” report, the top theme is value realization. We know that demonstrating value requires the model to be deployed. But the roadblocks to production appear to vary for different roles.

Data scientists juggle between multiple use cases with varied requirements. Having a dynamic workbench to manage dependencies is critical for them. Developers are most frustrated by the need to re-package the data scientists’ work into production-ready code. The SysAdmins have to operationalize the packaged code, so they are stumbled by meeting security standards.

If your team struggles with deployment, it’s time to sit everyone down in one (virtual) room and unpack where the bottleneck is. The leaders must recognize that driving cross-functional visibility can boost team efficiency.

Adopting a good #MLOps practice can bridge the gap between the scientific, the engineering, and the operations process. You might find the solution to be as simple as defining and adopting the desired ML workflow for the team. Interested to find out what MLOps best practices are❓ Comment below to find out   👇👇👇