An icon representing the LayerNext MetaLake.

A unified repository for all of your AI data

A data lake for collecting, searching, curating and analyzing computer vision data.

A diagram showing how the LayerNext product is working.A diagram showing how the LayerNext product is working.

Visualize & Explore

Connect your cloud data storage and visualize data from anywhere using your browser. Create standardized metadata and tagging schema to enforce data quality standards.

  • Integrates with your S3, GCP, Azure cloud storage buckets
  • Fast search with proprietary indexing module
  • Create and standardize metadata and tags
  • Support images, video and other files types such as GPS data
An image showing the LayerNext's integration with cloud storage buckets.
An image showing how the search queries are working.

Search & Filter

Quickly find the data you need by searching and filtering on metadata, labels and tags. Easily share data amongst your team for rapid collaboration without the need for downloading or copying data.

  • Build dynamic search queries with built in query interface
  • Search data with metadata, tags, labels and other content types
  • Generate shareable links for dataset, image or video collections

Analyze & Iterate

Gain analytical insights into your data and debug your models faster with LayerNext Analytics . Debug model errors and dataset problems by comparing performance across model training experiments.

  • Visualize metadata and label distributions in datasets
  • Compare the performance matrixes of training experiments
  • Rapidly iterate training experiments with multiple datasets
An image showing the machine annotation and human annotation.

Integrate & Connect

Use LayerNext Python SDK to integrate the MetaLake with your team’s unique workflow. Stream inference data into the MetaLake for storage and analysis and connect the MetaLake to your model build process.

  • Integrate 3rd party applications with the MetaLake using an SDK
  • Monitor and control MetaLake inflows and outflows
  • Use LayerNext’s CLI tool to manage datasets at training endpoints

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