{"id":17893,"date":"2022-09-14T19:26:16","date_gmt":"2022-09-14T13:56:16","guid":{"rendered":"https:\/\/cigniti.com\/blog\/?p=17893"},"modified":"2023-01-31T17:01:29","modified_gmt":"2023-01-31T11:31:29","slug":"accelerate-data-annotation-zastra-ml","status":"publish","type":"post","link":"https:\/\/www.cigniti.com\/blog\/accelerate-data-annotation-zastra-ml\/","title":{"rendered":"Know the Finest Way to Accelerate your Data Annotation Efforts"},"content":{"rendered":"

A decade ago, getting data was a critical task in every industry. Today, we see data flowing into the organization\u2019s database from everywhere. Organizations can access massive amounts of data from sales, marketing, customer experience, finance, operations, human resources, and other departments.<\/p>\n

The requirement of today is how to use this data efficiently and effectively. New-age technologies such as data and insights, visualization, Artificial Intelligence (AI), Machine Learning (ML), etc., have come into the picture to address this problem. We can now draw better insights from the data, visualize effectively, and use AI and ML to get results faster than ever expected.<\/p>\n

According to a recent report by Grand View Research, the size of the worldwide market for data annotation tools is anticipated to reach $1.6 billion<\/strong> by 2025.<\/p>\n

Why data annotation is crucial in the ML model?<\/strong><\/p>\n

One breakthrough technology that is presently used in almost every industry and has a breadth of applications is Machine Learning. It is one of the popular subfields of Artificial Intelligence and is used almost everywhere, such as in healthcare, finance, marketing, consumer behavior, autonomous cars, gaming, food processing, satellite imagery, green energy companies, utilities, sustainable energy, etc.<\/p>\n

Data scientists model different ML algorithms to draw meaningful insights from a given set of data. They use different models on the same data to gather different insights based on the needs of the business. The crucial part behind the success of any ML model is the process called \u201cdata annotation\u201d.<\/p>\n

Importance of data annotation in AI<\/strong><\/p>\n

Data annotation is the workhorse behind AI and ML algorithms. It is the process of labeling the data available in various formats like text, audio, video, or images. Labeled data sets are required for machine learning so that the machines can clearly understand the input patterns. The success of the ML model depends on how well your data is annotated. But unfortunately, valuable manpower spends days annotating the data rather than creating and deploying models.<\/p>\n

Data annotation is a heavily time-consuming process, where 40-50%<\/strong> of the time of an ML project goes into labeling the data rather than deploying the ML models.<\/p>\n

Problems faced by data scientists<\/strong><\/p>\n

Data annotation is a laborious process. The real-life data is messy, unstructured, and enormous. This means data scientists need to spend a major amount of time on the preparation of the data rather than spending their valuable time on building robust models.<\/p>\n

According to surveys of data scientists, \u201c76% of data scientists view data preparation as the least enjoyable part of their work\u201d and \u201cdata scientists spend most of their time massaging rather than mining or modeling data\u201d.<\/p>\n

To address this challenge, we at Cigniti have developed a solution to speed up this data annotation process. We developed a platform called \u201cZastraTM<\/sup><\/a><\/strong>.\u201d<\/p>\n

Minimize data annotation efforts with ZastraTM<\/sup><\/strong><\/p>\n

ZastraTM<\/sup> is an end-to-end, enterprise-grade annotation workflow platform that minimizes Data Annotation efforts and maximizes Collaboration.<\/p>\n

It uses state-of-the-art Active Learning methods to reduce annotation efforts by up to 70% and delivers high-quality detection, classification, and segmentation of image and video datasets.<\/p>\n

Why ZastraTM<\/sup> is designed?<\/strong><\/p>\n

The following important points motivated us to design ZastraTM<\/sup> and address the problems faced by Data scientists.<\/p>\n