3 Reasons Why Data Collection Won’t Work Without Human Interference

In this special guest feature, David Karandish, CEO of Capacity, suggests that while humans are teaching machines to consistently deliver the highest-quality results, we’re also filling in the gaps in the data collection along the way. Without a human in the loop, your machine learning models may pollute datasets and deliver poor results. The only way to ensure you receive the best results is for humans to work alongside the machine learning models in your data collection process.