Welcome back to a new ML lesson for managers and engineerswhere I share machine learning lessons drawn from mistakes and misconceptions I encounter in the industries that drive my company, NextML.
Today, we are faced with a common mistake even among the most experienced machine learning engineers and data scientists. I've seen it across industries, in companies large and small, and in a wide range of use cases.
The mistake is flooding algorithms with simple examples during training, which leads to slower learning, poorer generalization, and greater sensitivity to outliers.
Even more critical for most businesses, slow training of machine learning algorithms drains their finances faster than necessary.
Note: In my experience, managers make poor decisions about their machine learning and ai strategy because they don't understand the technology. I want to change that by providing lessons with a good balance between technical understanding and underlying reasoning.
