End-to-End Calibration Workflow Automation in Pharma Using Databricks and AI

Great care is taken in the pharmaceutical business especially when dealing with various instruments where small differences in calibration are considered significant.Most manual calibration workflows are slow, inaccurate and involve the use of many resources.In this paper, an end-to-end Databricks and artificial intelligent based calibration workflow automation system is proposed.Integrated with the cloud capacities of Databricks and using innovative machine learning, the solution addresses data processing, identifies anomalies, predicts maintenance and optimises reporting.The coordination of AI indexes empowers momentary decision-making throughout the chains which thus lessens downtime and enhances compliance.Such an approach's practical advantages are also exemplified by the case studies from the leading pharmaceutical organizations.We show that our proposed approach is beneficial in terms of efficiency, accuracy and operational costs based on a comparative study.This paper also looks at issues touched on above like data privacy, integration issues as well as validation protocols.In general, the proposed framework is the foundation to establish the calibration standards within the contemporary calibration processes in the pharmaceutical industry.