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Labelbox and Databricks: Better together

OVERVIEW

Data teams use Databricks and Apache Spark™ to analyze structured data, but may struggle to apply the same analysis to unstructured, unlabeled data (specifically in the form of images, video, etc). Combining Databricks and Labelbox gives you an end-to-end environment for unstructured data workflows - a query engine built around Delta Lake, fast annotation tools, and a powerful Machine Learning environment.

Finding faster and more cost effective ways to convert unstructured data into structured data is highly beneficial towards supporting more advanced use-cases built around their companies’ unique, unstructured datasets.

About Labelbox

Labelbox is the leading training data platform for AI applications. We enable enterprises to rapidly annotate, manage and iterate on labeled data. Better ways to create and manage training data leads to higher quality data, more accurate machine-learning models, and faster iterations to improve the AI applications which deliver enterprise growth.

Productionize your unstructured data

Access the LabelSpark library (a connector between Databricks and Labelbox) to connect an unstructured dataset to Labelbox, programmatically set up an ontology for labeling, and return the labeled dataset in a Spark DataFrame.

LabelSpark library and documentation available on Github

Key benefits

  • Powerful querying tools

    Access faster search capabilities for your ML projects

  • Annotate seamlessly

    Easily produce high-quality labeled data

  • Model-assisted labeling

    Accelerate iteration by leveraging automated labeling

See Labelbox + Databricks in action