Labelbox•August 24, 2021
On July 21st 2021, Labelbox had the opportunity to present during AWS Dev Day alongside Databricks. The presentation covered how ML teams of any size can use Databricks, Labelbox, and Amazon S3 to build a robust ML pipeline. Below are three important topics we discussed during the event.
A training data platform enables AI/ML teams to create high quality training data quickly, enabling them to get their algorithms to production faster. Labelbox was built with three specific requirements in mind:
The presentation included a live demo of how ML teams can use the Labelbox connector for Databricks (available for free on Github) to:
Watch the recording of the demo below.
Many ML teams are using data warehouses (such as Databricks) and MLOPs platforms (such as MLFlow) to create a reliable pipeline as they develop and train their algorithms. Adding Labelbox to this mix can help teams create high quality training data fast, with multiple advanced workflows.
One of these is model-assisted labeling (MAL), which enables teams to import model outputs as pre-labeled data, which can then be reviewed, corrected, and improved by domain experts or labelers and used to make higher quality training data. Teams can use this workflow to:
In either case, MAL can significantly increase the speed and efficiency with which your ML team creates training data.
Watch the demo and visit our partner page to learn how your can use Labelbox with Databricks. You can also download our guide to discover how to choose a training data platform that significantly improves the quality and efficiency of your ML pipeline.
Labelbox•September 29, 2021
Labelbox lands in London: The key to a performant ML model lies within your training data
In a data-centric approach to machine learning, no element is more essential in your ML endeavors than creating and maintaining high-quality training data. Many ML teams fail due to two common but critical mistakes.