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Data annotation - A rising demand in AI & ML

Data annotation is an important part of the two fastest growing technologies which are artificial intelligence(AI) & Machine learning(ML). One of the infamous examples is that of Tesla cars. Tesla is a successful example of data annotation today, however, did you know that Tesla cars once faced an issue where the cars could not remember the street signs & markers?

The auto-pilot mode in Tesla would recognise a sign “lane ends in 100m” and post driving those 100m, would have trouble changing the lanes. The engineers at Tesla used a spatial recurring network video module. This module was designed to create a cache of data that a model can refer to when trying to make predictions about the road. Their solution needed a lot of data annotators performing annotation in the vector space by using special tools resulting in shortening the amount of time it takes to annotate a huge amount of data.

Data annotation, basically, aids in automated applications for computers and machines to recognise different sets of data. The data sets are created by labeling different data in required formats like text, images and videos. These formats enable the machine to understand input patterns. The labeled data is then further fed to the machine to train it for the given data, enabling it to be used across different fields. The data that’s generated is mostly unstructured. It could be an audio, video, text, graphic or a mix format. Data annotation allows a model to classify the data depending on its functionalities & parameters and act accordingly.


The importance of data annotation in today’s day and age

Data annotation is important when measuring the success rates of projects in which image & speech recognition are widely used. One of the few industries where data annotation is of importance are healthcare,construction, retail, ecommerce and marketing industries. These industries are directly dependent on accurate dataset.

Accurate datasets are an extremely important part of data annotation. DesiCrew works smartly and spends sufficient time to avoid even the smallest mistakes while labeling the data. We understand that any error can result in less accuracy of the project. The labeling part is done from the start of the project in supervised learning of the model. This allows the model to take less time in learning.

If we look at the unsupervised learning part, which is the process of training a machine without actually integrating it with a trained datasets, the machine trains itself using hidden patterns & insights in data. In this case, sooner or later one needs to get the data annotated to get better and accurate results.

For example, when it comes to search engines a ton of data is generated by us. This generated data is then fed to the machine for the sole purpose of learning about our favorite searches and with this new available data the machine continues to train itself to generate more enhanced and useful information that could be related to the person. All the search engines like Google & Bing, use machine learning technology to improve their performance and accuracy of the results which are based on the end users behavior history.

Furthermore, various chatbots like Google assistant, Siri, Alexa & Cortanam respond to end-user queries based on the speed of their demand when they are trained & labeled with accurate datasets.


Let us see how Data annotation benefits a business

The rise of AI & ML in industries like healthcare, automobiles, construction and many more industries has helped businesses automate their operations. Artificial Intelligence not only minimizes human efforts but reduces cost as well. This cost is used for investing in training ML algorithms to generate accurate and precise results for betterment of a business.

As we are all aware, Data is the digital gold for any business as it helps not only understand the market but also, improve in the respective fields. Business collecting and labeling datasets based on the context, type, need and feature tend to have better user experience. Better user experience leads to brand loyalty among consumers and also attracts more people.

The data that we generate if not annotated tends to cause massive issues in user experience for any business. Business, thereby, continuously update and label new datasets. This can be done by an automated method or have humans involved in data annotation.

With the end-to-end labeling technology, one can promote efficiency in business by eliminating middle layers, multiple steps and taking the confusion out of problems.

DesiCrew offers world class services along with proper tools for precise data annotation processes. Tools like intelligent menu digitization, workflow automation,automation tool for translation & transcription are to name a few amongst them.

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