Data quality is clearly paramount for a successful AI project, but how difficult is it to achieve?
When you have massive data to label for machine learning, it makes sense to outsource it. But what happens when your data is sensitive, protected, or private? Here’s a quick ...
Your choices about tooling and workforce will be important factors in your success as you design, test, validate, and deploy any ML model.
Crowdsourcing seems to offer a cheap option for training machine learning models, but it’s rarely as inexpensive as it seems. Here are some of the hidden costs of the crowd.
AI innovators rely on external teams to structure data for ML algorithms. But scaling quality data requires the right people & processes in your tech stack.