AI/ML Based Anomaly Detection for Improved Scientific and Regulatory Outcomes

AI/ML Based Anomaly Detection for Improved Scientific and Regulatory Outcomes

An open data ecosystem can help you leverage artificial intelligence and machine learning (AI/ML) to detect scientific data anomalies in a timely manner for improved scientific and regulatory outcomes. Watch this webinar to learn more. 

Key takeaways:

  • Data replatforming in the cloud helps biopharma companies integrate scientific data from different sources, streamline their processes, and accelerate compliance outcomes.
  • Data outlier detection helps to eliminate human error, reduce variability, and produce a stronger path for regulatory submissions.

Data fragmentation poses a challenge

Here are some of the typical data challenges that life sciences organizations face:

  • Disperse big data from different sources, which can lead to data loss.
  • Lack of data context, which can make it difficult to trace errors to their origin.
  • Inability to access raw data in perpetuity as the volume of data increases.

To address these challenges, biopharma companies are increasingly replatforming their data to the cloud.

Make the most of your data with a science-aware platform

With TetraScience, you can integrate all sources that produce data and centralize it in the cloud. Our platform collects information about how data was obtained and includes audit trail capabilities.

TetraScience also engineers data in a vendor-agnostic format that is Findable, Accessible, Interoperable, and Reusable (FAIR). FAIR data enables you to leverage AI, ML and data science tools to detect anomalies and flag compliance issues as soon as they arise. 

Watch our webinar to learn how to make the most of your anomaly detection machine learning algorithms with TetraScience.