You probably don’t need us to tell you this, but machine learning is expensive. Whether it’s data storage, data warehousing, rising compute costs, models that need retraining, hiring the best and brightest in a competitive marketplace, or data labeling, spinning up production-ready machine learning projects can run a pretty penny. Some of those costs are simply baked into the process–some models simply need to be retrained to stay relevant in changing and fluid domains, for example.
Data labeling, on the other hand, doesn’t need to cost you what it does today.
The biggest issue most companies have with labeling right now is that they’re still operating under a paradigm where more data is better. Adding additional training data is sometimes seen as a panacea when in fact, some of that data is redundant or actively harmful to your models. And if you’re running supervised or semi-supervised ML projects, that likely means that you need to label more and more data. It’s not hard to see how costs can run away from you. In fact, for many of us, we’ve worked on projects that became infeasible for this very reason.
But labeling really is a necessity for most deep learning problems. And while cleaning and reducing the size of your training sets is never a bad idea, there are plenty of other ways to save money and time on data labeling when working with a third party. It takes a little work but your finance department will thank you.
Want some tips on how to reduce the time and money you spend labeling data? Check out the recording of a webinar we did earlier this week and we’ll show you just that.