How Exscientia reduced the time it takes to monitor and prepare models from days to hours
Company Size
200-1,000
Region
- Europe
Country
- United Kingdom
Product
- Seldon Deploy
Tech Stack
- AI
- Machine Learning
- MLOps
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Innovation Output
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - API Integration & Management
Applicable Industries
- Healthcare & Hospitals
- Pharmaceuticals
Applicable Functions
- Product Research & Development
Use Cases
- Machine Condition Monitoring
- Predictive Quality Analytics
Services
- Data Science Services
- System Integration
About The Customer
Exscientia plc is an AI-powered drug discovery organization. The company's mission is to design and develop novel, precision-engineered drugs with an improved probability of clinical success for the benefit of patients. AI is at the heart of their business and is the determining factor in the success or failure of projects. The company has grown and expanded its reach and goals, necessitating the need for enterprise-grade scale solutions. The company's unique model deployment process is entirely automated, resulting in thousands of models being delivered, monitored, and retrained without human interaction.
The Challenge
Exscientia plc is an AI-powered drug discovery organization that relies heavily on the accuracy and stability of its models. The company's model deployment process is unique as it is entirely automated, resulting in thousands of models being delivered, monitored, and retrained without human interaction. However, as Exscientia expanded its reach and goals, it needed an enterprise-grade scale solution. The team was looking for additional operational efficiencies and other ways to debug and stabilize models. The existing open-source deployment solution and inference platform were no longer sufficient for their growing needs.
The Solution
Exscientia decided to implement Seldon Deploy to assist with monitoring and managing models in production, enabling them to scale and speed up the debugging process. The team had to decide whether to change their model code when moving to a new MLOps platform. Changing the code could have slowed down the entire project, so they decided to keep the same code and port over all their existing models. One of the benefits of implementing Seldon was that the code didn't matter to Seldon, and they were able to fit the architecture to the model code. This decision saved a lot of time and allowed Exscientia to maintain the stability and reliability of the many models now in production.
Operational Impact
Quantitative Benefit
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