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🎉 We released Spotlight 1.6.0 check it out

9 posts tagged with "data curation"

· 8 min read
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.
visualizationdata curationsynthetic data generationproductiondata-centric AIpythonclustering

· 10 min read
The Industrial AI Canvas can be a useful tool for planning data and ml-based projects.
Data-centric AIdata curationproject managementuse case assessment

· 4 min read
If you work in ML-based acoustics, the annual DCASE challenge is a great resource to learn about new state-of-the-art methods. We built an enriched dataset for the condition monitoring task that can be downloaded from Huggingface and explored with Spotlight in just five minutes.
data-centric AIdata curationanomaly detectionacoustics

· 7 min read
We have just released the open version of our data curation software Renumics Spotlight. It is intended for cross-functional teams who want to be in control of their data and data curation processes. In this post I would like to share our ideas behind this product.
data-centric AIdata curation

· 8 min read
Data collection for condition monitoring has several pitfalls, potentially leading to data that is not suitable for training robust machine learning models. The data problems resulting from the data collection include but are not limited to the presence of failures in the recording equipment, the dominance of specific operating conditions, or mislabeled audio samples. In this article, we will thus help you to ask the right questions and equip you with a checklist you can use when collecting and preparing data for your condition monitoring use case.
condition monitoringdata curationaudiodata explorationEDA

· 11 min read
Data collection for condition monitoring has several pitfalls, potentially leading to data that is not suitable for training robust machine learning models. The data problems resulting from the data collection include but are not limited to the presence of failures in the recording equipment, the dominance of specific operating conditions, or mislabeled audio samples. In this article, we will thus help you to ask the right questions and equip you with a checklist you can use when collecting and preparing data for your condition monitoring use case.
condition monitoringdata curationaudiodata explorationEDA

· 6 min read
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.
visual inspectiondata curationLookout for Visionproductiondata-centric AIMLOpscloud

· 10 min read
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.
visual inspectiondata curationLookout for Visionproductiondata-centric AIMLOpscloud

· 3 min read
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.
unwanted biasmodel evaluationdata curation