Abstract: 

According to Gartner, 90% of AI projects will fail.

Before companies spend time and money in machine learning, they must understand the importance of using the right data to train their machine learning models.

Data preparation and labeling is essential for training AI and machine learning models; it’s what makes them truly intelligent. But in many cases, a deeper understanding of information is needed to correctly annotate and label data for AI. When it comes to analyzing, reviewing and extracting data from complex documents like medical records or derivative contracts, AI needs humans-in-the-loop.

Innodata’s Chief Product Officer, Rahul Singhal, will explain how to align machine learning with humans-in-the-loop to generate, capture, monitor and analyze high volumes of diverse content with greater efficiency and accuracy.

The ODSC audience will learn:
• How to use unstructured data
• How to create ontologies
• Best practices of data annotation and labeling
• How to leverage subject matter experts

Bio: 

Coming Soon!