Machine Learning for Automated Visual QC
Transforming pharmaceutical manufacturing with real-time image classification and quality assurance
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We help organizations across sectors apply machine learning to solve real-world challenges — from prototype to production. Our consulting services span the full ML lifecycle and are tailored to meet domain-specific needs such as compliance, interpretability, and scalability.
- Opportunity Discovery & Business Impact Mapping
- Model Development, Tuning & Evaluation
- Computer Vision & Signal Processing Applications
- Predictive Modeling for Operations, Sales & Risk
- ML Ops, Monitoring & Model Lifecycle Management
- Training & Enablement for Non-Technical Teams
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ML-Powered Visual Quality Inspection in Pharma Manufacturing
Enhancing pharmaceutical manufacturing with computer vision to ensure defect-free products at scale.
Client & Goal Overview
Client Type
Pharmaceutical Manufacturer (Global Operations)
Challenge
Manual visual inspection of packaging and labelling in pharmaceutical production lines was error-prone, costly, and difficult to scale. The company aimed to explore whether machine learning — specifically computer vision — could automate detection of visual anomalies (e.g., misaligned labels, print defects, surface contamination) while meeting regulatory standards and maintaining product traceability.
Our Role
Design and deployment of an ML-powered computer vision system integrated into manufacturing lines, trained to detect diverse types of visual defects with high precision and regulatory compliance. End-to-end support covered solution design, data labelling, model deployment, and user training.
Project Journey & Results
Project Scope
- Designed a deep learning model trained on thousands of annotated images from real production data.
- Built a defect detection pipeline using convolutional neural networks (CNNs) optimised for edge deployment.
- Deployed the model on cameras integrated into the packaging line (real-time, on-device inference).
- Enabled a human-in-the-loop validation dashboard for regulatory traceability and review.
- Integrated system alerts and batch reports into existing quality assurance workflows.
Technical Stack
- Model: ResNet50 + custom CNN architecture (TensorFlow, PyTorch hybrid)
- Pipeline: Python + ONNX for model optimization
- Data Platform: Azure Blob Storage + Label Studio for annotation
- Edge Deployment: NVIDIA Jetson TX2 with optimized inference runtime
- Interface: Custom dashboard (Streamlit + FastAPI) with manual override and report export
- Monitoring: MLflow + Azure Monitor integration for performance tracking
Results
- Improved packaging defect detection accuracy from 82% (manual) to 96% (automated).
- Reduced inspection time per unit by 70%.
- Enabled 24/7 inline inspection with minimal false positives.
- Compliant reporting framework aligned with GxP and FDA CFR Part 11.
🟧 Automated image-based inspection with 96% accuracy.
🟧 Quality control time reduced by 70%.
🟧 Inline compliance-ready reporting integrated with existing QA systems.
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