Achieve Autonomous AI for the Enterprise

The potential of AI to comprehend all forms of data and improve business outcomes such as customer satisfaction, operational productivity, business agility, and risk management is its most significant advantage. However, not all businesses are benefiting equally. While some firms are experiencing technical debt due to a lack of AI skills, others are having difficulty moving applications into production due to insufficient infrastructure and tooling. Models in production require continuous monitoring, retraining when drifts reach a certain threshold, and governance throughout their existence.

Businesses need a simpler method to examine data, construct models, and go from workbenches to deploying models at scale in production as we race towards a future driven by yottabytes of data and trillions of models. To get AI to work for organizations, ModelOps capabilities will be a must. Advanced AutoML techniques, on the other hand, are removing the requirement for specialists to apply AI. As a result, the technical debt and complicated maintenance procedures associated with revalidating the commercial value of models continuously are reduced.

We'll look at AI through the lens of use cases and infrastructure limitations in this Fireside chat, and how these difficulties may be overcome to get the greatest potential business outcomes.

The Fireside Chat will focus on:

• Data and AI Market Outlook from 451 Research
• AI Practitioners Perspective from Innominds’ Experiences