Data Annotation - Outsourcing v/s In-house - ROI and Benefits. So what is Data Annotation? Data annotation is a process to tag or label data making it easier for the algorithms to understand and process. AI experts tag data through specialized tools. Here are the Advantages Of Outsourcing Data Annotation Work.
A 2018 report revealed that we generated close to 2.5 quintillion bytes of data every single day. Contrary to popular belief, not all the data we generate can be processed for insights. For data that can be used for training machine learning algorithms, the data has to be classified.
If you ask a layman how Artificial Intelligence models and algorithms work, they would tell you that it involves just three steps:
Data is fed to the algorithms
The algorithms process the data
The desired results are obtained
But in reality, this is not how an AI model works and with this outlook, we are completely missing a crucial layer that defines the entire algorithm’s capability to produce efficient and accurate results – data annotation.
In simple words, data annotation or labeling is a process in which humans (inevitable parts of artificial intelligence) tag or label data to make it easier for the algorithms to understand and process. AI experts tag data such as videos, text, audio, images, and other forms of data through specialized tools with human-in-the-loop. Only when the data is tagged can a machine can actually work on it.
However, the actual debate stems at this point as several companies out there have varied opinions on where they would like to get their data annotated. While some companies lean towards having an in-house team or using existing manpower and resources to annotate data, others prefer to outsource data annotation to third party vendors.
Both have their own set of pros and cons and if you’re someone who is stuck at that exact point in the process, this post will help bring you closer to making the right decision for you.
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