AI Showcase: No-code AI for Predicting Product Impurities | AIChE
Sponsor: Canvass AI | This webinar is sponsored by Canvass AI and reflects their views, opinions, and insights. Attendance is free.

AI Showcase: No-code AI for Predicting Product Impurities

The demand for digital skills is outstripping supply. Are you relying on third-party consultants or a data science team to transform your data and build artificial intelligence (AI) models to solve complex problems? There’s a better way. Join this session to learn how no-code Industrial AI platforms put chemical engineers in the role of digital engineers in charge of extracting the predictive insights valuable to you.  

In one hour, you’ll learn how to empower chemical engineers with no coding experience or expertise to use AI to solve a plethora of complex problems, such as predicting impurity in the product stream of a typical chemical refining process. By doing so, they’ll help make your processes more streamlined and efficient. 

Don’t miss this opportunity to explore a platform that can easily aid any chemical engineer to take data analytics into their own hands. No longer will you need to fill the skills gap by outsourcing to third parties or hiring additional in-house digital engineers. You’ll know how to put your chemical engineers in the driver’s seat with no-code AI for predicting product impurities.

You’ll learn:  

  • The benefits of using no-code AI platforms in your operation
  • How to easily gather real-world chemical data set to predict impurity in your product stream
  • What you need to get started: A checklist of requirements

Sponsored webinars bring technical information from reputable firms and suppliers to AIChE members. The content reflects the views, opinions, and recommendations of the sponsoring organization. AIChE does not warrant or represent, expressly or by implication, the correctness or accuracy of the content of the information presented. As between (1) the AIChE, the presenter and author(s) of this work, their employers, and their employers' officers and directors, and (2) the user/viewer of this work, the user/viewer accepts any legal liability or responsibility whatsoever for the consequence of its use or misuse. Contact information for attendees of this webinar, including email address, will be shared with the sponsoring firm. You will always have the opportunity to unsubscribe from email from that organization.

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