What is WhyLabs?

WhyLabs is a cloud-based platform that helps you keep an eye on your AI and machine-learning models after they’re deployed. It checks things like data quality, model performance, and whether the model is starting to behave differently over time.

Let's break it down

  • Cloud-based platform: a service you access over the internet, no need to install software on your own servers.
  • Keep an eye on: continuously watch or monitor.
  • AI and machine-learning models: computer programs that learn patterns from data to make predictions or decisions.
  • Deployed: the model is live and being used in a real application, not just in a test environment.
  • Data quality: how clean, complete, and accurate the input data is.
  • Model performance: how well the model’s predictions match reality (accuracy, speed, etc.).
  • Behave differently over time: “drift,” meaning the model’s predictions get worse because the world changes.

Why does it matter?

If a model silently starts making bad predictions, it can cause financial loss, damage trust, or even create safety hazards. WhyLabs alerts you early, so you can fix problems before they hurt your business or users.

Where is it used?

  • An online retailer monitors its product-recommendation engine to catch drops in click-through rates.
  • A bank watches its fraud-detection model for sudden spikes in false positives that could block legitimate customers.
  • A hospital tracks a diagnostic AI tool to ensure it remains accurate as new patient data arrives.
  • An autonomous-vehicle company checks sensor-data pipelines so the driving model doesn’t degrade in new weather conditions.

Good things about it

  • Real-time monitoring and instant alerts.
  • Easy integration with popular ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).
  • Visual dashboards that make complex metrics understandable.
  • Automated detection of data drift and performance decay.
  • Supports both small experiments and large-scale production systems.

Not-so-good things

  • Subscription cost can be high for startups or small teams.
  • Requires some engineering effort to set up data pipelines and logging.
  • Customization options may be limited compared to building a home-grown monitoring stack.
  • Learning curve for non-technical stakeholders to interpret the dashboards.