A lot of data is daily generated from camera and radar/lidar sensors from self-driving cars and robots. The data need to be stored and synchronized across multiple locations and storage layers.
Efficiently storing petabyte scale data has been a significant challenge. Snark offers a TensorDB, array data warehouse solution specialized for storing large volumes of array-like data including image frames and point clouds.
Intelligently querying the data, similar to SQL, hasn’t been possible. Snark offers an Array-SQL solution that you can query by specifying the image type e.g. pull out all images of the front siding camera that have been captured during the night with a bicycle in front.
Snark offers building plug-in-play data pipelines and efficiently generating datasets to train deep learning models for image segmentation and object detection.
Snark will connect to your current storage, ingest the data, and show you the results within days.
Our team of machine learning experts will help you with data pre-processing, model selection, feature engineering support as much or as little as you need.
Enterprise-grade cloud computing optimized down to cents. Leverages the most cost-efficient resources such as spot instances.
Too much time is spent on setting up the data. With well-designed data pipelines, rapid iterations of machine learning experiments will result in models with superhuman accuracy much faster.Learn more
At Snark, we have released an open-source package called Hub to manage large scale datasets. The package lets you represent large arrays on the cloud or on the remote storage as if they are local numpy arrays.Learn more