How To Build a Scalable Data Annotation Strategy

As you may know, data science teams spend about 80% of their time creating and managing training data. The usual issues are often related to poor in-house tooling, labeling re-work, finding the needed data, and the difficulties associated with collaborating and iterating on distributed teams' data. Frequent workflow changes, large-volume datasets, and a lack of proper data training workflow can hinder a company's development. These issues worsen when the company grows too quickly, as is often the case with startups, regardless of the industry. A perfect example of such a need for a scalable training-data strategy comes from the highly-competitive autonomous vehicles industry.