Harnessing multiple data sources to build innovative AI products || WBN

AI teams today use a variety of different data types for their projects, including text, computer vision, and specialized formats such as OCR, DICOM, geospatial imagery, and more. Compiling enough high-quality data to train an algorithm can be a challenge with any of these data types, as they can be expensive and/or hard to find.

In this webinar, you’ll learn how leading AI teams tackle these bottlenecks by combining multiple sources of data to gain valuable insights, leverage both external and internal data, and ultimately develop more robust AI products. We’ll also cover:

When to employ different sources to improve predictions in a given ML iteration cycle
Examples of applied use cases and how companies are combining both subjective and objective information from sources such as OCR, images, and text
What cutting-edge AI teams are accomplishing and what’s possible via a training data platform
A walkthrough of how to use products like Catalog to understand and search all of your unstructured data (e.g., consumer reviews for consumer products)
How to combine workflows and data types when your data is unstructured by using ML frameworks like Databricks

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

Chris Amata

Solution Engineer, Labelbox

Featured Speakers

Harnessing multiple data sources to build innovative AI products

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

ML Unboxed: How to diagnose and improve model performance

January 12: 11am PT

January 13: 8am PT/11am ET/4pm GMT

ML UNBOXED

Mark Ghannam

Solutions Engineer, EMEA, Labelbox