About the Case Study
In collaboration with a leading pharmaceutical company, we designed and implemented a robust machine learning (ML) workflow to streamline hypothesis evaluation, improve model accuracy, and automate key processes. This end-to-end solution integrates over 20 diverse data sources, creating a powerful knowledge graph that accelerates scientific research.
Challenges & Objectives
Our client, a major player in the pharmaceutical industry, sought to:
- Improve computational efficiency through optimised ML engineering
- Enhance model accuracy and research methodologies
- Establish a continuous training and inference loop for real-time learning
- Develop a user-friendly interface for managing ML experiments
- Deploy a scalable and automated infrastructure for seamless ML operations (MLOps)
Solution
Through a customised ML workflow, we tackled these challenges by:
- Integrating Multiple Data Sources – A sophisticated knowledge graph was built, unifying information from over 20 structured and unstructured datasets.
- Optimising Model Evaluation – We streamlined the process for testing novel scientific hypotheses, ensuring a faster and more accurate validation cycle.
- Automating MLOps – Implemented a continuous training and inference loop, along with automated monitoring and model registry, reducing manual overhead and improving reliability.
- Deploying an Intuitive ML Experimentation Platform – Researchers now have an interactive workspace to efficiently query models, monitor performance, and iterate rapidly.
Key Technologies Used
Our technology stack leveraged industry-leading cloud and ML services, including:
- Amazon SageMaker – For scalable machine learning training and inference
- Amazon RDS (PostgreSQL) – For secure and efficient data storage
- Amazon SNS & Lambda – For automated notifications and event-driven processing
- Amazon S3 – For reliable data storage and retrieval
- Kubernetes & REST APIs – For seamless deployment and integration
Results & Impact
- Faster Drug Discovery – Our ML-driven target identification process validated a novel drug target, which has now progressed to the next research stage.
- Improved Efficiency – Streamlined model development and deployment, reducing time-to-insight for scientific teams.
- Scalable & Future-Proof Infrastructure – The solution is designed to grow with evolving research needs, ensuring long-term innovation.
Conclusion
By combining advanced ML engineering, automation, and cloud-native technologies, we delivered a customised ML workflow that enhances research efficiency and accelerates drug discovery. This collaboration showcases the power of machine learning in transforming pharmaceutical research—from hypothesis testing to real-world impact.
