Our new AI-guided anomaly detector helps you find errors in engineering test data faster.
Monolith, a leader in AI-guided testing for engineers, has developed new tools to automate the inspection of engineering data to help you find more errors in your data faster, offload your engineers from tedious, repetitive work, and reduce expensive and time-consuming retesting. Without clean, error-free data, you can’t build accurate machine-learning models. Now, you can leverage the power of AI to find problems with your data quickly and effectively.
Monolith engineers have worked closely with a small group of customers to define and develop new tools optimized for inspecting engineering test data. In this webinar, Principal Product Engineer Dr. Joël Henry will demonstrate the new Anomaly Detector now available in the Monolith platform. We’ll review the most common data, sensor, and system errors encountered in engineering applications and how this new technology automates the process of finding them in your data.
Learning Objectives:
- Review the sources of common data errors in testing and validation labs.
- Understand the steps needed to train and apply the Anomaly Detector to find anomalies in your test data.
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Who Should Watch:
- Leaders in R&D and engineering responsible for product validation and certification testing.
- Business leaders interested in ways to use AI to accelerate time to market.
- Test engineers looking for smarter methods and tools to automate repetitive tasks faster and more accurately.
