ML monitoring that doesn’t miss a beat

ML observability

ML monitoring that doesn’t miss a beat

Continuously track performance, drift, and live issues with automated observability for ML systems.

Core features

Explore the platform

Monitor the performance of your live model

Keep tabs on model accuracy and health in production. Identify degradation or anomalies before they cause downstream damage.

Detect data and model drift

Automatically track shifts in feature distributions and output behavior. Get alerted to subtle changes in model performance.

Instant alerts

Get notified when tests fail or anomalies arise—so your team can respond before users are impacted.

Why it matters

Your models don’t fail all at once, they degrade over time

In production, machine learning models face real-world data, user drift, and changing distributions. Without the right observability, performance can silently degrade. Openlayer gives your team the tools to detect drift, monitor metrics, and respond fast—before it impacts the business.

Use cases

Proactive monitoring for any ML system

From demand forecasting to computer vision, Openlayer helps ML teams across industries track performance in production, get alerted to anomalies, and ensure reliability in real-world environments.

Why Openlayer

Built for modern ML operations

Continuous validation using live dataCatch issues early by testing every model against fresh production traffic in real time.

Built-in alerting and drift detectionGet instant Slack or email pings the moment performance dips or data distribution shifts.

Integrates with CI/CD and monitoring toolsDrop straight into your existing workflow, no re-platforming required.

Multimodal and deployment-agnostic supportTrack text, vision, tabular, or hybrid models wherever they run—cloud, on-prem, or at the edge.

Integrations

Deploy with confidence in any environment

Openlayer integrates with your inference layer, observability stack, and CI/CD tooling. Compatible with major cloud providers, logging systems, and production monitoring frameworks.

Customers

Real-time insights that make a difference

“We caught a critical drift issue within hours—not weeks—thanks to Openlayer's production monitoring.”

Head of Data Science at Insurance Company

FAQs

Your questions, answered

What is ML observability?

ML observability is the practice of monitoring live machine learning systems to detect performance degradation, data drift, and operational failures.

How is this different from standard model monitoring?

Openlayer combines monitoring with testing and drift detection—so you catch issues even when metrics look fine.

Can I use Openlayer with my existing models?

Yes, our platform integrates with any model or deployment environment with minimal setup.

What does alerting look like?

You’ll get real-time notifications through Slack, email, or your preferred incident response tool.

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ML observability