Description

Building a single machine learning model is one challenge. Running thousands of them in production — reliably, efficiently, and transparently — is another. As Vanderlande adopted its use of anomaly detection, we faced the reality that success wasn’t about the accuracy of one model, but about the MLOps infrastructure to scale, orchestrate, and monitor many.

We are two data scientists who gradually found ourselves tackling problems that look a lot more like ML engineering. In this session, we’ll share our journey in a practical and informal way, showing the real steps we took, the hurdles we hit, and the solutions we built.

We’ll walk through the orchestration layer that coordinates training and deployment for thousands of models, highlight practical solutions for scaling pipelines, and discuss how we keep the system resilient when failures occur. A short code snippet will illustrate how lightweight the deployment process can be, and we’ll close with a look at the monitoring framework that provides visibility and reliability across the entire fleet.

Whether you are just starting to scale your ML initiatives or already struggling with operational complexity, this talk will offer lessons learned and concrete practices for moving beyond one model.

Outline

1. Vanderlande’s use case (5 min)
- Vanderlande specific use case
- Anomaly detection at scale
- Why beyond one model became a necessity

2. Machine learning orchestration & pipeline (13 min)
- Intro: One model ML Architecture
- Architecture & orchestration design for multiple models
- Isolated pipelines to avoid cascading failures

3. Code snippet & peek into monitoring (7 min)
- Inference orchestrator code example
- CI/CD deployment pipeline
- Monitoring framework: jobs, models, anomalies at scale
- Lessons learned & what we’d do differently

4. Wrap-up & Q&A (5 min)
- Key takeaways
- Quick audience questions