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6 Insights to Overcome the Hidden Pitfalls of Data Annotation [ua]

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The growing demand for personalization, the benefits of automation, and the big data accessibility all lead to ML projects becoming commonplace in the modern business world. And while more people learn how to build successful ML models, there’s still a lot of misconceptions about supporting tasks. Specifically, data annotation might look like a small and simple job that, in reality, will take up a lot of your time and resources. Besides, it’s ridden with hidden pitfalls that depend on the complexity of the project, the available resources, and security risks.

I would like to lead you through the biggest of these problems and offer recommendations to avoid them. With the final checklist I’ll provide, you’ll be able to build a smart and effective data labeling strategy, whether you decide to annotate the data in-house or find an outsourcing partner.

Karyna Naminas
Label Your Data
  • CEO at Label Your Data
  • Outsourcing business C-level executive
  • Leader of a new vibrant AI startup
  • Passionate and curious about the new CV and NLP technologies
  • Helping AI companies to keep their focus on strategies and development
  • LinkedIn, Twitter
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