What is Data Labeling

1. What is Data Labeling? Everything a Beginner Needs to Know

2. What is data labeling In machine learning, data labeling is the process of identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it. For example, labels might indicate whether a photo contains a bird or car, which words were uttered in an audio recording, or if an x-ray contains a tumor. Data labeling is required for a variety of use cases including computer vision, natural language processing, and speech recognition. Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

3. Global Data Labeling Market AI models need to be trained extensively for being able to identify patterns, objects, and eventually make reliable decisions. This is where data labeling helps in labeling information or metadata, to focus on amplifying the understanding of the machines. As per the latest report the data labeling market is presumed to reach a massive valuation of $4.4 billion by 2023. View the full infographics to learn more: Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

4. 7 Data Labeling Challenges AI feeds on copious amounts of data to continually learn and evolve. Tagging objects within textual, image, scans, etc. enable algorithms to interpret the labeled data and get trained to solve real business cases. The task of labeling data must meet 2 essential parameters: quality & accuracy, however, it comes with several challenges. View the full infographics to learn 7 Data labeling challenges companies face. Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

5. Types of Data Labeling There are various types of data labeling modalities, depending on what type of data you deal in. Although you can segregate data labeling conceptually, the majority of problems in which AI models are being built to address them can fit into one (or many) of the below annotation tasks these include, text classification, audio transcription, image, and video labeling, semantic labeling, and content categorization, etc. View the full infographics to learn more: Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

6. 4 Key Steps in Data Labeling Data annotation is a detailed process and involves the following steps to categorically train AI models: Data Collection Data Labeling & Annotation Quality Assurance Deployment / Production Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

7. Factors to consider while choosing the right tool Selecting the right labeling tool to accurately train your AI models is of utmost importance. The right set of data labeling tools is synonymous with a credible data labeling platform that needs to be selected, keeping in mind a lot of factors. View the full infographics to know different factors that one should consider: Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

8. Build vs Buy Still confused as to which is a better strategy to get data labeling on track, i.e., Building a self-managed setup or Buying one from a third-party service provider. Here are the pros and cons of each to help you decide better: Source: https://www.shaip.com/blog/what-is-data-labeing-everything-a-beginner-needs-to-know/

9. Read the Data Annotation / Labeling Buyers Guide, or download a PDF Version. CLICK HERE TO DOWNLOAD