A Dynamic Model in Artificial Intelligence is a computational framework that can adapt and change its internal parameters or structure over time as it processes new, real-time, or streaming data. Unlike static models, which are trained once and then deployed, dynamic models are continuously or periodically retrained, making them particularly valuable in environments where the underlying data distributions evolve—a phenomenon known as model drift. In the life sciences and pharmaceutical manufacturing sectors, this is critical for applications like:
- Process Analytical Technology (PAT): Real-time adjustments to manufacturing parameters in a continuous manufacturing line based on live sensor data.
- Predictive Maintenance: Updating equipment failure probability based on continuous operational metrics.
- Clinical Trial Monitoring: Adapting patient risk stratification as new safety and efficacy data is collected.
Dynamic models ensure that predictions and decisions remain accurate and relevant, but they require robust Data Governance and validation protocols to maintain compliance with cGMP and regulatory standards.
