Tasks
- esign and optimize ML pipelines for manufacturing.
- Develop scalable cloud infrastructure for ML.
- Prepare, clean, and annotate data for quality training.
- Implement CI/CD pipelines for ML models.
The main challenge was to create a robust, scalable ML pipeline to process large volumes of manufacturing data in real-time, ensuring reliability and efficiency to support AI-driven co-pilot solutions for operator assistance.
Integration with R&D workflows; data privacy compliance.
Expert services reduce research time and costs by 15-20%, enhancing drug discovery success rates.
Integration with LIMS, compliance with regulatory standards and GMP.
Machine learning enhances HPLC capabilities, achieving a 20-25% increase in operational efficiency and reducing manual workload.
Integration with ERP/CRM systems; GDPR/HIPAA compliance.
ARIMA and LSTM models achieve 10-15% reduction in material waste.
Neural Network model for real-time sensor fusion to detect reactor issues, reducing failures by 40% and enhancing control capabilities.
Automated data collection and reporting, integrated systems, and enhanced decision-making with predictive analytics.
Developed an ML model for profiling transactions and clustering regular and irregular spending, improving user insights and app features.