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How to Build a Scalable Data-Annotation Strategy
On finding the right tool, hiring or outsourcing annotators, and ML-assisted annotation
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. Computer vision applied to self-driving vehicles is a complex and competitive market. Due to the complexity, the definition and scope of high-quality training data change frequently. If your team cannot adapt (including your ability to annotate data), customer dissatisfaction can cost you the whole business.
