Top
image credit: Adobe Stock

For AI model success, utilize MLops and get the data right

May 16, 2022

Artificial intelligence (AI) in the lab is one thing; in the real world, it’s another. Many AI models fail to yield reliable results when deployed. Others start well, but then results erode, leaving their owners frustrated. Many businesses do not get the return on AI they expect. Why do AI models fail and what is the remedy?

As companies have experimented with AI models more, there have been some successes, but numerous disappointments. Dimensional Research reports that 96% of AI projects encounter problems with data quality, data labeling and building model confidence.

Read More on Venture Beat