Successfully deploying your first AI model is a critical initial step towards a data-driven operations floor. Once you have developed and deployed your first successful model into production (live streaming data in and live predictions out) you’ll want to do more. Different models, different systems, in different parts of the plant.
The issue becomes how do you add the second, third and tenth model without incurring a huge amount of IT resources and long cycles of IT development. Plus, how do you keep it all running.
Scaling AI across multiple processes brings new complexities that must be mitigated to achieve true automation. In this session learn about the key considerations and requirements to scaling your AI strategy, including how to connect the different sources of data; managing dozens of models concurrently; and monitoring AI for success.
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