Harnessing Machine Learning for Enhanced Quality Control & Anomaly Detection in Cell Therapy Manufacturing
Time: 1:30 pm
day: Conference Day One
Details:
- Explore how machine learning empowers predictive modelling of product quality attributes, leveraging historical data to anticipate deviations in real-time and proactively maintain consistency in cell therapy manufacturing processes
- Delve into the role of machine learning algorithms in detecting anomalies and deviations from expected patterns, allowing for rapid identification of process failures or product inconsistencies. By flagging anomalies, ML-based systems enable timely corrective actions to minimise downtime and mitigate the risk of product loss
- Discuss the transformative impact of machine learning on quality control and anomaly detection, enabling cell therapy manufacturers to implement proactive measures that enhance process efficiency, product quality, and regulatory compliance
