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Machine Learning for Pharmaceutical Manufacturing and Quality Control Training
"Machine Learning for Pharmaceutical Manufacturing and Quality Control Training" teaches how to implement ML models for predictive maintenance, anomaly detection, and automated process control. Participants explore methods to improve consistency and regulatory compliance in pharma production. Cybersecurity is vital in these systems, as threats targeting ML-driven control platforms can disrupt manufacturing operations and compromise data integrity. The course includes techniques for safeguarding sensitive production environments against adversarial AI attacks and system breaches.
Provider Information
Tonex, Inc.
6275 W. Plano Parkway, Suite 500
Plano, TX 75093
Course Overview
None
Learning Objectives
- Understand the application of machine learning in pharmaceutical manufacturing.
- Analyze how ML improves quality control processes and defect detection.
- Evaluate the benefits of ML for optimizing production workflows and reducing waste.
- Identify key ML algorithms and their suitability for pharma manufacturing challenges.
- Discuss the cybersecurity risks associated with integrating ML into operational technology in manufacturing, such as data manipulation or intellectual property theft.
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):
Competency Areas
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.