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Tools for data science teams to Reduce Data Labelling costs
Loaded with intuitive and interactive features to tackle hard and time consuming problems
Connect
Virtually connect multiple data sources and associated meta-data databases to explore, search, and analyze visual data based on system-defined meta-data. Without the need to crete copies of data and meta-data, analysis can always be conducted with the most up-to-date versions.
Explore
Explore visual data on unlabeled datasets by combining traditional meta-data-based filtering with content feature-based latent structure exploration to better understand the inherent clustering or segmentation structure in the data set.
Search
Explore visual data on unlabeled datasets by combining traditional meta-data-based filtering with content feature-based latent structure exploration to better understand the inherent clustering or segmentation structure in the data set.
Analyze
Explore visual data on unlabeled datasets by combining traditional meta-data-based filtering with content feature-based latent structure exploration to better understand the inherent clustering or segmentation structure in the data set.
Compare
Identify novel data across multiple data sets to create more training points, evaluate labeling quality and consistency, determine data drifts, and more.
Benefits of using Data Explorer
Save time and resources
Reduce hours of work to minutes. Drastically cut the manual labor and time spent on data selection and curation.
Accelerate the path to accuracy
Reduce hours of work to minutes. Drastically cut the manual labor and time spent on data selection and curation.
Reduce the labelling spend
Reduce hours of work to minutes. Drastically cut the manual labor and time spent on data selection and curation.
Selection of Data has a cascading effect on Training Time, Resources and achieving Model Accuracy.
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"Designed for Data Science teams to accelerate the path to building Production Grade AI models"
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