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Why Data Annotation Redefines Scalability

Data Annotation is the reason your AI and ML models understand and make decisions the way they do, and without the training provided by data annotation, these models more likely than not, will remain clueless of the actions to be taken. 

But what exactly is data annotation?

Well, simply defined, data annotation is the process of using metadata to label data that is to be used for training purposes. Think of it like labelling your spice jars with name tags to easily understand what to pick when the need arises. But it’s a little more complicated than that. Supervised ML and AI models are taught how to recognize patterns when presented with different data, and unannotated data to qualify for becoming usable models. More like replacing your labelled jars with new ones (without labels) to see if you can recognize the spices without any help.

For example – You type in your everyday browser the word “contract”, the browser will try to understand your intent behind this query, which could either be that you are looking for a legal agreement related information or something related to contracting a virus. It is the algorithm that understands the intent because it has been trained how to recognize patterns, by human-powered data annotators.

And data annotation is not just text labelling, it is also image labelling, wherein the system is fed tons of images with labels and categories for it to understand and be able to differentiate between different objects.

For example – An autonomous vehicle that uses computer vision, knows when the light is green it’s time to move, and when its red its time to stop. It can understand the difference between these signals because of all the labelled images that were used to train the model in such a way that it can independently function with ease while making the right decisions.

It is because of that particular reason, the surge of image data annotation tools and services is being witnessed.
The global data annotation tools market size was valued at USD 494.0 million in 2020 and is expected to expand at a compound annual growth rate (CAGR) of 27.1% from 2021 to 2028, as per a research from Grand View Research.

Who is a Data Annotator?

Data Annotator is the specialist who categorizes content under different types and data sets, for ML and AI models to learn how to function.
This is a professional role wherein the person is responsible for annotating and anonymizing data while ensuring that they maintain the highest sets of standards as per the compliance discipline. Data Annotators also help with the aggregation and cleaning of data for ML applications.

Advantages of Data Annotation

  • Data Annotation makes your models more precise and smart
  • Better end user experience can be achieved with data annotation
  • Increased relevancy helps in making the models more reliable
  • Seamless processing of requests with respect to intent and sentiments is an added advantage of data annotation.

Data Annotation & ML – Power Combo of tomorrow?

Machine learning models are highly reputed in the industry for learning from the inputs provided to be able to make effective decisions with the help of the learnings and the training. With data annotation, these models are fed with labelled data sets, that can spearhead the training process and increase the accountability of the mechanism of ML models.

  • ML and Data Annotation have to go hand in hand for these models to function independently in later stages.
  • User queries can be quickly resolved with the help of data annotation training offered to the ML algorithms to grasp the emotion and sentiment.
  • Identifiers, keywords, and captions while annotating data are highly essential to make the ML and computer vision understand the working methodology.

The technological arena is expanding at a ferocious pace and data annotation is about to witness a major revamp. Organizations now need to have a deeper understanding of the data that they use to feed the models for accurate annotation, which is why experts from different domains are coming together to offer a varied blend of data annotation services and expertise with They will be seen working on – 

  • Enabling machines to assign metadata to images for annotation using mere keywords and captions.
  • Conducting frame by frame analysis of videos for precision annotation and detection
  • Using AI models to moderate huge amounts of content
  • Enhancing deep learning algorithms to make them more responsive.

What will it take for you to be a Data Annotation Specialist?

Data Annotation is an extremely detail-oriented career wherein the professional has to be extremely precise and specific about their labelling process, which is also what makes the job a highly lucrative one. Today, there is a need in the market for expert data annotators that can utilize specialized scripts and train the models in the best way possible. 

Some critical skills that all data annotation specialists must possess are – 

  • Understanding of AI/ML algorithms and models
  • Expertise in annotation software
  • Understanding of the detailed difference in content categories
  • Data Labeling guidelines awareness
  • Data scrubbing, web searching and suitable data collection

Data Annotation is the foundation of building smarter ML models that can sustain in an organization with higher scalability and enhanced mechanisms. Not only is it necessary to have efficient human-powered annotation done during the training, it is also equally important to have enough data to feed the algorithm. ML is transformed with the elevated intelligence during the labelling process, and it’s all thanks to data annotation.

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