Selecting samples for training robust surrogate models in simulation can be a real challenge. Active learning-like approaches where samples are selected iteratively can help overcome this challenge. We show how to apply such a procedure to save time and computational resources while making your surrogate model even more robust.

Building robust models for Visual Inspection in production settings can be a real challenge. Here, cloud services like Amazon Lookout for Vision promise relief for model training but have limitations regarding data curation. This article explores those potential shortcomings and shows how to improve over them to leverage these services to the fullest.

Making your data match the real-world data of your use-case is crucial for training a robust machine learning model. This post shows you how to interactively curate your data to adapt your data in an informed manner.

Ensuring label consistency is critical for building robust machine learning models. This post shows you how to achieve label consistency in a data centric way.

Machine Learning based systems for acoustic event detection typically require a vast amount of training data. Intelligent labeling techniques open up new possibilities for small data scenarios.

ML researchers typically iterate different model architectures on a fixed dataset that they often know very little about. In real use cases, it is often a good idea to focus most efforts on the data and not on the model.

Data-centric machine learning is an emerging paradigm. Is this a game-changer for ML in engineering and manufacturing?

Data-driven methods have arrived and promise to speed up product development. The AI-assisted Engineering Canvas helps to get started.

Generative ML methods have achieved tremendous success in image and audio processing. Can mechanical engineers benefit from this technology?