The training data practices used by the most successful AI teams || WBN

Having helped hundreds of AI teams across a wide range of verticals, we have discovered some commonalities in their ML workflows that lead to long-term project success and greater scalability. Seeing first-hand the key roadblocks and best practices of the most successful ML teams, we wanted to share how they’ve utilized certain processes, standards, and tools to more skillfully master how they work with training data.

In this webinar, you will learn some of the training data practices used by the most successful AI teams and how these have been applied in production use cases. We’ll also be answering live Q&A from the audience so that you can tailor these lessons into your own MLOps and accelerate your AI journey.

Learn how leading AI teams accomplish more by leveraging:

Advanced collaboration between domain experts, labelers, and data scientists
APIs for smooth integrations and delegated access for better access to data
Interpreting model performance and prioritizing areas for iteration
Monitoring on labeling performance across project levels and individual levels

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Raphael Jafri

Senior MLSE, Labelbox

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The training data practices used by the most successful AI teams

Just some of companies that Labelbox is working with to build AI applications:

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