What does AI operationalization mean? How can AI implementation move from the lab to real-world enterprise deployment?

Enterprises today are leveraging the power of AI to achieve their business goals more than ever. However, scaling AI is not easy. According to the International Institute for Analytics, only 15% of organizations deploy their ML models in production successfully. The reasons are — (a) Most enterprises are using AI for automating simple tasks, often in siloes. (b) Enterprises require systems that enable ML deployments at scale. To achieve operationalization, enterprises need to look beyond research labs and emphasize on real-word production deployment. They also need processes to ensure consistency, reliability, and measurability of impact.

At EdgeVerve, we’ve built a host of best practices framework for your deployment lifecycle to ensure ML model operationalization success.

Download the thought paper by Kumar Abhinav, Delivery Head – NIA Services, to learn the best practices to follow across your ML deployment lifecycle. These steps will help you operationalize AI throughout the enterprise and enable you to adopt AI at scale.

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