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How observability and model insight keeps AI profitable

In traditional software a mistake leads to an error when running it in production. But in machine learning the biggest problem isn’t when the code doesn’t run correctly.

It’s actually the opposite.

What worries data science teams is when no one notices that the environment has changed, but a model continues running normally. The business is making decisions off an outdated model but doesn’t find out until there is a huge financial loss.

Continuous benchmarking, testing, and observability are key for data science teams to deliver ongoing business value.

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Understanding drift and detecting it in production:

  • What is model drift?
  • How observability helps reduce data drift
  • How to monitor data drifting
  • Wallaroo’s model insights framework
  • Baseline generation with Wallaroo

How and when to retrain your models:

  • What is “good” model performance?
  • Measuring the performance of your models
  • How environment changes affect performance

Wallaroo levels the machine learning playing field:

Easy, Secure and Scalable Deployment
Launch 3X Faster
12.5X Faster Inferencing
80% Lower Server Costs