Most machine learning models fail not at training, but in production where customer trust and business outcomes are on the line. This overview and demo focuses on real insights into how to deploy, monitor, explain, and continuously improve models with confidence. Using a real-world e-commerce fraud detection example, we’ll show how the right planning and tools can give you the ability to catch drift, explain decisions, and keep models aligned with your business.
About
Learnings
- Where to start building in observability early to stop models failing in production.
- How to manage through explaining predictions, detecting drift, and monitoring performance with confidence.
- Best practices for building feedback loops and embedding observability into your ML workflows.
- Live Demo: see how to apply these principles to e-commerce fraud detection with Seldon.
Andrew Wilson
Andrew Wilson is the Head of Solutions at Seldon, where he has spent over three years bridging technical innovation and business transformation. With a background in Industrial Engineering, he combines a system-level perspective with a keen eye for user-centric design.
Andrew specializes in leveraging data science, MLOps, and web development to deliver impactful AI-driven solutions. Known for building strong customer relationships, he transforms complex technical challenges into strategic opportunities, helping organizations unlock the full value of their data and AI capabilities.
Complex, real-time use cases is what we do best
Talk with a expert to explore how Seldon can support more streamlined deployments for real-time, complex projects like fraud detection, personalization, and so much more.
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