Join this webinar on APRIL 27, 11am - Noon, EST, to learn how an open data ecosystem can drive AI/ML based anomaly detection to increase data confidence, eliminate human error, reduce variability, and produce a stronger path for regulatory submissions.

AI/ML can be a powerful tool for data analysis and instrument control. Its use now can be as straightforward as putting a sample in and getting a sample out of a laboratory instrument such as a mass spectrometer. However, maximizing highly complex AI/ML algorithms requires both large volumes of actionable data and the expertise to design a proper analysis and determine the quality and meaning of the results.

Adopting digital technologies creates possibilities for both significant rewards and greater risks. Performing automated and sophisticated analyses on a wide range of organized data can generate profoundly improved scientific and regulatory outcomes. 

Yet, the promise of this will be stymied without an open data ecosystem, where data liquidity can drive automation through a powerful, compliant analytical tool. Additionally, as digital transformation of the pharmaceutical industry progresses, the FDA continues to update its guidance and expectations, making audit trails and other compliant activities even more crucial.

We will discuss how to accelerate insights and mitigate risks in the regulated portions of your value chain using AI/ML. By making scientific data a business priority, we’ll show how to increase data confidence, eliminate human error, reduce variability, and produce a much stronger path for regulatory submissions.

During the webinar, you’ll learn:

• How to increase confidence in your scientific data using AI/ML
• Best practices for automating scientific data capture and analysis
• How to minimize variability and create repeatability of results
• What the new compliance trends are and how are they impacting pharma
• How to overcome the biggest challenges in compliance through automation