To oversimplify the priority of data training before code is, when you train your dog to fetch a ball, the end result is to make the dog active and make you happy.

The focus, in this situation is to give your dog the data to work with - the ball, what to do with the ball, and what to do once it has the ball.

If your dog doesn't understand the data, then he won't fetch the ball. The same is true in developing sophisticated machine learning models.

Your training data's efficacy, variety, and structure is more important than code.

Here's what Michael Chui, a Mckinsey global institute partner has to say...

“The current generations of AI are what we call machine learning (ML) — in the sense that we’re not just programming computers, but we’re training and teaching them with data,”

AI feeds heavily on data.

Andrew Ng, former AI head of Google and Baidu, states data is the rocket fuel needed to power the ML rocket ship.

He also mentions that companies and organizations which are taking AI seriously are eager to acquire the correct and useful data.

Moreover, as the number of parameters and the complexity of problems increases, the need for high-quality data at scale grows exponentially.

We at Traindata Inc., have a team with more than 15 years of experience of working at Yahoo! and have built a service to train, clean, classify, and structure large quantities of data for your AI and ML projects.

Talk to us about your AL/ML data training challenges today.