Data science practitioners have always faced one pressing challenge: access to large quantities of high-quality data. Collecting and annotating data good enough to train effective AI models is a time consuming and labor-intensive process: in fact data scientists spend 80% of their time cleaning and preparing data instead of training models.
While the solution for many is to buy off-the-shelf datasets, there are different types of training data needed for each step of an AI project, and purchasers need to know what type of data to look for. For example:
- \\ Should data be domain specific or generic?
- \\ Does an off-the-shelf dataset suit the stage of the process or should a customized dataset be used?
- \\ Should your model be trained on multimodal collection or annotation?
These are just a few of the decisions Data Scientists need to make when deciding what off-the-shelf training data to purchase.
Watch the discussion between Bradley Metrock (This Week in Voice) and Daniela Braga (Founder & CEO of DefinedCrowd) as they attempt to answer these, and other questions around data procurement for artificial intelligence.