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Top Four Myths About Outsource Data Annotation Services

This PDF copy is describing about the top four myths about outsource data annotation services with examples to clear unambiguous stories of data annotation outsourcing and what are the exact truth about such myths. The document is shared by the experts at Cogito encourgaong the AI and ML companies, why they should outsource their data annotation needs and get the better quality data for model training at affordable cost.

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Top Four Myths About Outsource Data Annotation Services

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  1. Top Four Myths About Outsource Data Annotation Services

  2. Clear The Myth From Your Mind And Read Here The Top Four Myths About Outsourcing Data Annotation With Few Suggestions To Know How To Outsource The Data Annotation. Getting Annotated data is not possible, only experts can do this job better.

  3. Myth 1: Is My Data is Safe and Remains Confidential with Annotators?

  4. Myth 1: This is one of the top reason, companies try to annotate the data at their in-house resources. Actually, loosing the data could be dangerous for businesses, but its not true, outsourcing companies also operate professionally and keep the clients data in safe hands.

  5. Myth 2: In-house Annotators Can Do This Job Or Don’t Need Expertize

  6. Myth 2: This is also a hearsay, that annotators are not skilled or not specialized enough to annotate with that quality, instead in- house annotators can do this job easily. Actually, nobody knows better than you what is your model, but it doesn’t mean you can cater other aspects, rather specialists can also understand your requirements.

  7. Myth 3: You Think Your Use Case is Too Complicated than others

  8. Myth 3: Many AI or ML companies assume that their use case is too much complicated, that data annotation companies will unable too meet the requirements. But its not true, having a great discussion with both team members can easily clear the exact requirements, and annotators can deal with any type of industry for providing the training data.

  9. Myth 4: Hiring third-party Annotators are Expensive

  10. Myth 4: You have already spend handsome amount of money on machine learning engineer or data scientist to develop a fully- functional model for you. Compromising with other inputs will give you poor results. And training a AI-enabled model through computer vision is a difficult job when you assign such task to internal resources.

  11. Thanks for Watching

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