{"id":4511,"date":"2023-08-07T12:20:44","date_gmt":"2023-08-07T12:20:44","guid":{"rendered":"https:\/\/pickl.ai\/blog\/?p=4511"},"modified":"2024-08-07T04:56:13","modified_gmt":"2024-08-07T04:56:13","slug":"guide-to-data-labelling","status":"publish","type":"post","link":"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/","title":{"rendered":"A Comprehensive Guide to Data Labelling"},"content":{"rendered":"<p><b>Summary:<\/b><span style=\"font-weight: 400;\"> Data labelling involves annotating data to provide context for Machine Learning models, enhancing their accuracy and effectiveness. This process is vital across industries, including healthcare, finance, and e-commerce. By employing best practices and utilising appropriate tools, organisations can ensure high-quality labelled data, leading to improved model performance and insights.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#What_is_Data_Labelling\" >What is Data Labelling?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Importance_of_Data_Labelling\" >Importance of Data Labelling<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Techniques_for_Data_Labelling\" >Techniques for Data Labelling<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Image_Annotation\" >Image Annotation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Text_Annotation\" >Text Annotation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#_Audio_Annotation\" >\u00a0Audio Annotation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Video_Annotation\" >Video Annotation<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Best_Practices_for_Data_Labelling\" >Best Practices for Data Labelling<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Define_Clear_Guidelines\" >Define Clear Guidelines<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Use_Qualified_Labelers\" >Use Qualified Labelers<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Implement_Quality_Control_Measures\" >Implement Quality Control Measures<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Leverage_Technology\" >Leverage Technology<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Iterative_Feedback\" >Iterative Feedback<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Scale_with_Automation\" >Scale with Automation<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Use_Cases_of_Data_Labelling\" >Use Cases of Data Labelling<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Autonomous_Vehicles\" >Autonomous Vehicles<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Healthcare\" >Healthcare<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#E-commerce\" >E-commerce<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Social_Media\" >Social Media<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Customer_Support\" >Customer Support<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Financial_Services\" >Financial Services<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Agriculture\" >Agriculture<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Challenges_in_Data_Labelling\" >Challenges in Data Labelling<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Time-Consuming\" >Time-Consuming<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Subjectivity\" >Subjectivity<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Scalability\" >Scalability<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Quality_Assurance\" >Quality Assurance<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Cost_Implications\" >Cost Implications<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Data_Privacy_and_Security\" >Data Privacy and Security<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Future_Trends_in_Data_Labelling\" >Future Trends in Data Labelling<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Automation_and_AI-Assisted_Labelling\" >Automation and AI-Assisted Labelling<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-33\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Crowdsourcing\" >Crowdsourcing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-34\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Synthetic_Data_Generation\" >Synthetic Data Generation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-35\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Enhanced_Collaboration_Tools\" >Enhanced Collaboration Tools<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-36\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Focus_on_Quality_Over_Quantity\" >Focus on Quality Over Quantity<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-37\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-38\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-39\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#How_Does_Data_Labelling_Impact_the_Development_of_AI_And_Machine_Learning_Models\" >How Does Data Labelling Impact the Development of AI And Machine Learning Models?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-40\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#What_Industries_Benefit_the_Most_from_Data_Labelling\" >What Industries Benefit the Most from Data Labelling?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-41\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/#How_Can_Businesses_Measure_the_ROI_Of_Data_Labelling_Projects\" >How Can Businesses Measure the ROI Of Data Labelling Projects?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 id=\"introduction\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><b>Introduction<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Data labelling is a critical process in the field of <\/span><a href=\"https:\/\/pickl.ai\/blog\/unsupervised-machine-learning-models-types-applications\/\"><span style=\"font-weight: 400;\">Machine Learning (ML)<\/span><\/a><span style=\"font-weight: 400;\"> and Artificial Intelligence (AI). It involves annotating raw data with meaningful labels that enable algorithms to learn and make predictions.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This guide provides an in-depth look at data labelling, its importance, various techniques, best practices, challenges, and real-world use cases.<\/span><\/p>\n<h2 id=\"what-is-data-labelling\"><span class=\"ez-toc-section\" id=\"What_is_Data_Labelling\"><\/span><b>What is Data Labelling?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Data labelling, also known as data annotation, is the process of identifying and tagging data points with specific labels that provide context. This allows Machine Learning models to understand the data and make informed predictions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The types of data that can be\u00a0 labelled\u00a0 include images, text, audio, and video.For example, in a computer vision task, data labelling may involve drawing bounding boxes around objects in images and assigning labels like &#8220;car,&#8221; &#8220;person,&#8221; or &#8220;tree.&#8221;\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In <\/span><a href=\"https:\/\/pickl.ai\/blog\/introduction-to-natural-language-processing\/\"><span style=\"font-weight: 400;\">Natural Language Processing (NLP)<\/span><\/a><span style=\"font-weight: 400;\">, data labelling could involve identifying parts of speech or sentiment in text data. Types of data in data labelling<\/span><\/p>\n<p><b>Structured Data<\/b><span style=\"font-weight: 400;\">: This includes data that is organised in a predefined manner, such as databases and spreadsheets. Examples include customer information, sales records, and sensor data.<\/span><\/p>\n<p><b>Unstructured Data<\/b><span style=\"font-weight: 400;\">: This type of data lacks a predefined format and is often text-heavy or multimedia. Examples include social media posts, emails, images, audio recordings, and videos.<\/span><\/p>\n<p><b>Semi-Structured Data<\/b><span style=\"font-weight: 400;\">: This is a mix of structured and unstructured data, such as JSON files or XML documents, where some elements may be organised while others are not.<\/span><\/p>\n<p><b>Explore more about data types, its classification and examples by <\/b><a href=\"https:\/\/pickl.ai\/blog\/data-classification-overview-types-and-examples\/\"><b>clicking here.<\/b><\/a><\/p>\n<h2 id=\"importance-of-data-labelling\"><span class=\"ez-toc-section\" id=\"Importance_of_Data_Labelling\"><\/span><b>Importance of Data Labelling<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Data labelling is fundamental for supervised learning, where models learn from\u00a0 labelled\u00a0 data to make predictions on unseen data. The quality of the labelled data directly impacts the performance of Machine Learning models. Poorly labelled data can lead to inaccurate predictions and reduced model effectiveness.<\/span><\/p>\n<p><b>Improved Model Accuracy<\/b><span style=\"font-weight: 400;\">: High-quality\u00a0 labelled\u00a0 data enhances the accuracy of Machine Learning models, enabling them to make better predictions.<\/span><\/p>\n<p><b>Enhanced Understanding<\/b><span style=\"font-weight: 400;\">:\u00a0 labelled\u00a0 data helps models understand the relationships between different data points, leading to more robust learning.<\/span><\/p>\n<p><b>Facilitates Automation<\/b><span style=\"font-weight: 400;\">: It is essential for automating processes in various industries, from healthcare to finance, by enabling machines to perform tasks that require human-like understanding.<\/span><\/p>\n<p><b>Data-Driven Insights<\/b><span style=\"font-weight: 400;\">:\u00a0 labelled\u00a0 data can help organisations derive insights from their data, allowing for better decision-making and strategy formulation.<\/span><\/p>\n<p><b>Compliance and Regulation<\/b><span style=\"font-weight: 400;\">: In industries like finance and healthcare, proper data labelling can help ensure compliance with regulations regarding data usage and privacy.<\/span><\/p>\n<p><b>Read More:<\/b><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/difference-between-data-observability-and-data-quality\/\"><b>Difference between Data Quality and Data Observability.<\/b><\/a><\/p>\n<h2 id=\"techniques-for-data-labelling\"><span class=\"ez-toc-section\" id=\"Techniques_for_Data_Labelling\"><\/span><b>Techniques for Data Labelling<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">There are several techniques used for data labelling, each suited to different types of data and use cases. Here are some common methods:<\/span><\/p>\n<h3 id=\"image-annotation\"><span class=\"ez-toc-section\" id=\"Image_Annotation\"><\/span><b>Image Annotation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Image annotation involves labelling images with relevant tags or bounding boxes. This is essential in computer vision tasks, such as object detection and image segmentation.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Bounding Boxes<\/b><span style=\"font-weight: 400;\">: Used to identify objects within images by drawing rectangles around them. For instance, in an autonomous vehicle system, bounding boxes can help identify pedestrians and other vehicles.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Semantic Segmentation<\/b><span style=\"font-weight: 400;\">: Involves labelling each pixel of an image to classify different regions. This is particularly useful in medical imaging, where precise identification of areas is crucial.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Polygon Annotation<\/b><span style=\"font-weight: 400;\">: Used for objects with irregular shapes, where labelers outline the object with polygons. This technique is often used in applications like satellite imagery analysis.<\/span><\/li>\n<\/ul>\n<h3 id=\"text-annotation\"><span class=\"ez-toc-section\" id=\"Text_Annotation\"><\/span><b>Text Annotation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Text annotation is crucial for natural language processing tasks. It involves labelling text data for various applications.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Named Entity Recognition (NER)<\/b><span style=\"font-weight: 400;\">: Identifying and labelling entities such as names, dates, and locations within text. For example, in a news article, NER can help identify key figures and events.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Sentiment Analysis<\/b><span style=\"font-weight: 400;\">: Labelling text based on sentiment, such as positive, negative, or neutral. This is widely used in social media monitoring and customer feedback analysis.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Part-of-Speech Tagging<\/b><span style=\"font-weight: 400;\">: Assigning grammatical labels to words in a sentence. This helps in understanding the structure and meaning of sentences for various NLP applications.<\/span><\/li>\n<\/ul>\n<h3 id=\"audio-annotation\"><span class=\"ez-toc-section\" id=\"_Audio_Annotation\"><\/span><b>\u00a0Audio Annotation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Audio annotation involves labelling audio clips for tasks such as speech recognition and sound classification.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Transcription<\/b><span style=\"font-weight: 400;\">: Converting spoken language into written text. This is essential for creating subtitles or for voice recognition systems.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Sound Event Detection<\/b><span style=\"font-weight: 400;\">: Labelling specific sounds or events within an audio clip, such as a dog barking or a car honking. This is useful in surveillance and environmental monitoring.<\/span><\/li>\n<\/ul>\n<h3 id=\"video-annotation\"><span class=\"ez-toc-section\" id=\"Video_Annotation\"><\/span><b>Video Annotation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Video annotation is similar to image annotation but involves labelling frames in a video.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Object Tracking<\/b><span style=\"font-weight: 400;\">: Labelling and tracking objects as they move through video frames. This is crucial in applications like surveillance and sports analytics.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Action Recognition<\/b><span style=\"font-weight: 400;\">: Identifying and labelling specific actions or events occurring in the video. This is widely used in security systems and sports analytics to analyse player movements.<\/span><\/li>\n<\/ul>\n<h2 id=\"best-practices-for-data-labelling\"><span class=\"ez-toc-section\" id=\"Best_Practices_for_Data_Labelling\"><\/span><b>Best Practices for Data Labelling<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Data labeling is essential for machine learning success, ensuring accuracy and reliability. Implement best practices like clear guidelines, thorough training, and quality control to enhance labeled <\/span><a href=\"https:\/\/pickl.ai\/blog\/how-to-scale-your-data-quality-operations-with-ai-machine-learning\/\"><span style=\"font-weight: 400;\">data quality<\/span><\/a><span style=\"font-weight: 400;\"> and model performance. To ensure high-quality labelled data, organisations should follow best practices during the data labelling process:<\/span><\/p>\n<h3 id=\"define-clear-guidelines\"><span class=\"ez-toc-section\" id=\"Define_Clear_Guidelines\"><\/span><b>Define Clear Guidelines<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Establish clear labelling guidelines that outline the labelling process, criteria, and examples. This helps labelers understand the expectations and reduces ambiguity. Providing annotated examples can serve as a reference for labelers.<\/span><\/p>\n<h3 id=\"use-qualified-labelers\"><span class=\"ez-toc-section\" id=\"Use_Qualified_Labelers\"><\/span><b>Use Qualified Labelers<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Engage experienced labelers who understand the domain and can accurately annotate the data. Training labelers on specific tasks can improve labelling quality. In some cases, domain experts may be necessary for specialised fields like<\/span><a href=\"https:\/\/pickl.ai\/blog\/data-science-applications-in-healthcare\/\"><span style=\"font-weight: 400;\"> healthcare <\/span><\/a><span style=\"font-weight: 400;\">or finance.<\/span><\/p>\n<h3 id=\"implement-quality-control-measures\"><span class=\"ez-toc-section\" id=\"Implement_Quality_Control_Measures\"><\/span><b>Implement Quality Control Measures<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Incorporate quality control processes, such as peer reviews and random sampling, to ensure the accuracy of labelled data. This helps identify and correct errors. Automated tools can also be used to monitor labelling consistency.<\/span><\/p>\n<h3 id=\"leverage-technology\"><span class=\"ez-toc-section\" id=\"Leverage_Technology\"><\/span><b>Leverage Technology<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Utilize labelling tools and software that streamline the labelling process and improve efficiency. Many tools offer features like automated labelling suggestions and collaborative workflows. Some popular tools include Labelbox, Supervisely, and VGG Image Annotator.<\/span><\/p>\n<h3 id=\"iterative-feedback\"><span class=\"ez-toc-section\" id=\"Iterative_Feedback\"><\/span><b>Iterative Feedback<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Provide continuous feedback to labelers to improve their performance. Regularly updating guidelines based on feedback and new insights can enhance the labelling process. Creating a feedback loop encourages labelers to learn and adapt.<\/span><\/p>\n<h3 id=\"scale-with-automation\"><span class=\"ez-toc-section\" id=\"Scale_with_Automation\"><\/span><b>Scale with Automation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Wherever possible, leverage Machine Learning algorithms to assist in the labelling process. Semi-automated labelling can help speed up the process, allowing human labelers to focus on more complex tasks while algorithms handle simpler annotations.<\/span><\/p>\n<h2 id=\"use-cases-of-data-labelling\"><span class=\"ez-toc-section\" id=\"Use_Cases_of_Data_Labelling\"><\/span><b>Use Cases of Data Labelling<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-13266\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-10.jpg\" alt=\"Use Cases of Data Labelling\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-10.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-10-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-10-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-10-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-10-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-10-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-10-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-10-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-10-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-10-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Data labelling is applied across various industries and use cases. Here are some notable examples:<\/span><\/p>\n<h3 id=\"autonomous-vehicles\"><span class=\"ez-toc-section\" id=\"Autonomous_Vehicles\"><\/span><b>Autonomous Vehicles<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In the development of self-driving cars, data labelling is crucial for training computer vision models to recognize objects on the road. Labelled data helps models identify pedestrians, traffic signs, and other vehicles, enabling safe navigation. For instance, companies like Tesla and Waymo rely heavily on labelled datasets to train their autonomous driving systems.<\/span><\/p>\n<h3 id=\"healthcare\"><span class=\"ez-toc-section\" id=\"Healthcare\"><\/span><b>Healthcare<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data labelling is used in <\/span><a href=\"https:\/\/pickl.ai\/blog\/bioinformatics-scientists\/\"><span style=\"font-weight: 400;\">healthcare<\/span><\/a><span style=\"font-weight: 400;\"> to annotate medical images, such as X-rays and MRIs. Labelled data helps train models to detect anomalies, such as tumours or fractures, aiding in diagnosis and treatment planning. For example, radiologists may label images to train AI systems that assist in identifying diseases like pneumonia or breast cancer.<\/span><\/p>\n<h3 id=\"e-commerce\"><span class=\"ez-toc-section\" id=\"E-commerce\"><\/span><b>E-commerce<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">E-commerce platforms use data labelling for product categorization and recommendation systems. By labelling product images and descriptions, models can better understand customer preferences and provide personalised recommendations.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For instance, Amazon uses labelled product data to enhance its search algorithms and improve user experience.<\/span><\/p>\n<h3 id=\"social-media\"><span class=\"ez-toc-section\" id=\"Social_Media\"><\/span><b>Social Media<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Social media companies employ data labelling for content moderation. Labelled data helps models identify inappropriate content, such as hate speech or graphic violence, ensuring a safer online environment. Platforms like Facebook and Twitter utilise labelled datasets to train their moderation algorithms.<\/span><\/p>\n<h3 id=\"customer-support\"><span class=\"ez-toc-section\" id=\"Customer_Support\"><\/span><b>Customer Support<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data labelling is utilised in customer support to train chatbots and virtual assistants. By labelling customer inquiries and responses, organisations can improve the accuracy of automated responses and enhance user experience. For example, companies like Zendesk and Intercom rely on labelled data to train their customer support AI.<\/span><\/p>\n<h3 id=\"financial-services\"><span class=\"ez-toc-section\" id=\"Financial_Services\"><\/span><b>Financial Services<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In the finance sector, data labelling is used for fraud detection and risk assessment. By labelling transaction data as legitimate or fraudulent, models can learn to identify suspicious activities. Banks and financial institutions leverage labelled datasets to enhance their fraud detection systems.<\/span><\/p>\n<h3 id=\"agriculture\"><span class=\"ez-toc-section\" id=\"Agriculture\"><\/span><b>Agriculture<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data labelling is increasingly being used in agriculture for precision farming. Drones equipped with cameras can capture images of crops, which are then labelled to identify plant health, pest infestations, or nutrient deficiencies. This information helps farmers make informed decisions about crop management.<\/span><\/p>\n<h2 id=\"challenges-in-data-labelling\"><span class=\"ez-toc-section\" id=\"Challenges_in_Data_Labelling\"><\/span><b>Challenges in Data Labelling<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-13267\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-7.jpg\" alt=\"Challenges in Data Labelling\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-7.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-7-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-7-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-7-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-7-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-7-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-7-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-7-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-7-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image4-7-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Data labelling presents several challenges, including inconsistencies in annotations, time-consuming processes, and the need for skilled personnel. Organisations must address these issues to ensure high-quality labelled data for effective machine learning.<\/span><\/p>\n<h3 id=\"time-consuming\"><span class=\"ez-toc-section\" id=\"Time-Consuming\"><\/span><b>Time-Consuming<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data labelling can be a labour-intensive process, especially for large datasets. Organisations must allocate sufficient resources and time to ensure accurate labelling. The time required can vary significantly depending on the complexity of the labelling task.<\/span><\/p>\n<h3 id=\"subjectivity\"><span class=\"ez-toc-section\" id=\"Subjectivity\"><\/span><b>Subjectivity<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Labelling can be subjective, leading to inconsistencies in annotations. Different labelers may interpret guidelines differently, resulting in variations in labelled data. This subjectivity can be mitigated through clear guidelines and continuous training.<\/span><\/p>\n<h3 id=\"scalability\"><span class=\"ez-toc-section\" id=\"Scalability\"><\/span><b>Scalability<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">As datasets grow, scaling the labelling process becomes challenging. Organisations need to find efficient ways to manage and label large volumes of data. This may involve employing more labelers or utilising automated tools to assist in the process.<\/span><\/p>\n<h3 id=\"quality-assurance\"><span class=\"ez-toc-section\" id=\"Quality_Assurance\"><\/span><b>Quality Assurance<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Ensuring the quality of labelled data is crucial but can be difficult. Organisations must implement robust quality control measures to maintain high standards. Regular audits and reviews can help identify areas for improvement.<\/span><\/p>\n<h3 id=\"cost-implications\"><span class=\"ez-toc-section\" id=\"Cost_Implications\"><\/span><b>Cost Implications<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data labelling can be costly, especially when hiring skilled labour or using specialised tools. Organisations must weigh the costs against the potential benefits of high-quality labelled data. Outsourcing to specialised data labelling companies can sometimes be more cost-effective.<\/span><\/p>\n<h3 id=\"data-privacy-and-security\"><span class=\"ez-toc-section\" id=\"Data_Privacy_and_Security\"><\/span><b>Data Privacy and Security<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In industries like healthcare and finance, data labelling must adhere to strict privacy regulations. Ensuring that sensitive data is handled securely during the labelling process is paramount. Organisations should implement data anonymization techniques and secure storage solutions.<\/span><\/p>\n<h2 id=\"future-trends-in-data-labelling\"><span class=\"ez-toc-section\" id=\"Future_Trends_in_Data_Labelling\"><\/span><b>Future Trends in Data Labelling<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">As Machine Learning and AI continue to evolve, so too will the methods and technologies used in data labelling. Here are some trends to watch:<\/span><\/p>\n<h3 id=\"automation-and-ai-assisted-labelling\"><span class=\"ez-toc-section\" id=\"Automation_and_AI-Assisted_Labelling\"><\/span><b>Automation and AI-Assisted Labelling<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The use of AI and Machine Learning to assist in the labelling process is expected to grow. Automated tools can help label simpler data points, allowing human labelers to focus on more complex tasks.<\/span><\/p>\n<h3 id=\"crowdsourcing\"><span class=\"ez-toc-section\" id=\"Crowdsourcing\"><\/span><b>Crowdsourcing<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Crowdsourcing is becoming a popular method for data labelling, allowing organisations to tap into a larger pool of labelers. Platforms like Amazon Mechanical Turk enable businesses to access a diverse workforce for labelling tasks.<\/span><\/p>\n<h3 id=\"synthetic-data-generation\"><span class=\"ez-toc-section\" id=\"Synthetic_Data_Generation\"><\/span><b>Synthetic Data Generation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Synthetic data generation involves creating artificial data that mimics real-world data. This can help reduce the need for extensive labelling by providing pre- labelled\u00a0 datasets for training models.<\/span><\/p>\n<h3 id=\"enhanced-collaboration-tools\"><span class=\"ez-toc-section\" id=\"Enhanced_Collaboration_Tools\"><\/span><b>Enhanced Collaboration Tools<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">As remote work becomes more common, tools that facilitate collaboration among labelers will gain importance. Features like real-time feedback, version control, and integrated communication will enhance the labelling process.<\/span><\/p>\n<h3 id=\"focus-on-quality-over-quantity\"><span class=\"ez-toc-section\" id=\"Focus_on_Quality_Over_Quantity\"><\/span><b>Focus on Quality Over Quantity<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Organisations are increasingly recognizing the importance of high-quality\u00a0 labelled\u00a0 data over sheer volume. Investing in thorough training and quality control measures will become a priority.<\/span><\/p>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><b>Conclusion<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Data labelling is a foundational step in the Machine Learning pipeline, enabling models to learn from labelled data and make accurate predictions.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By understanding the importance of data labelling, employing best practices, and addressing challenges, organisations can harness the power of labelled data to drive innovation and improve decision-making across various industries.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">As the demand for high-quality labelled data continues to grow, investing in effective data labelling strategies will be essential for organisations looking to leverage Machine Learning and <\/span><a href=\"https:\/\/pickl.ai\/blog\/ai-and-machine-learning-courses\/\"><span style=\"font-weight: 400;\">Artificial Intelligence<\/span><\/a><span style=\"font-weight: 400;\"> successfully.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By embracing data labelling as a critical component of their AI initiatives, businesses can unlock new opportunities and enhance their competitive advantage in an increasingly data-driven world.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In summary, data labelling is not just a task; it is a strategic investment that can significantly impact the success of Machine Learning projects. By prioritising quality, leveraging technology, and continuously improving processes, organisations can ensure they are well-equipped to navigate the complexities of data labelling and maximise the value of their data.<\/span><\/p>\n<h2 id=\"frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><b>Frequently Asked Questions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"how-does-data-labelling-impact-the-development-of-ai-and-machine-learning-models\"><span class=\"ez-toc-section\" id=\"How_Does_Data_Labelling_Impact_the_Development_of_AI_And_Machine_Learning_Models\"><\/span><b>How Does Data Labelling Impact the Development of AI And Machine Learning Models?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data labelling provides the essential training data for AI models to learn and make accurate predictions.<\/span><\/p>\n<h3 id=\"what-industries-benefit-the-most-from-data-labelling\"><span class=\"ez-toc-section\" id=\"What_Industries_Benefit_the_Most_from_Data_Labelling\"><\/span><b>What Industries Benefit the Most from Data Labelling?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Industries like healthcare, autonomous vehicles, finance, and e-commerce heavily rely on data labelling for AI applications.<\/span><\/p>\n<h3 id=\"how-can-businesses-measure-the-roi-of-data-labelling-projects\"><span class=\"ez-toc-section\" id=\"How_Can_Businesses_Measure_the_ROI_Of_Data_Labelling_Projects\"><\/span><b>How Can Businesses Measure the ROI Of Data Labelling Projects?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Evaluate ROI by comparing model accuracy, operational efficiency, and revenue generated with the cost of labelling.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Data labelling is essential for Machine Learning, ensuring models learn accurately from annotated datasets.\n","protected":false},"author":9,"featured_media":13268,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[2],"tags":[1528,1531,1532,1529,1530,1533],"ppma_author":[2170,2607],"class_list":{"0":"post-4511","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-machine-learning","8":"tag-data-labeling","9":"tag-data-labeling-tool","10":"tag-how-to-add-data-labels-in-google-sheets","11":"tag-labeled-data-in-machine-learning","12":"tag-labelled-and-unlabelled-data","13":"tag-which-approach-is-used-for-automatic-labelling"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Unleashing the Power of Data Labelling in Machine Learning<\/title>\n<meta name=\"description\" content=\"Data labelling is for training Machine Learning models, enabling accurate predictions. Explore techniques and tools for data labelling.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A Comprehensive Guide to Data Labelling\" \/>\n<meta property=\"og:description\" content=\"Data labelling is for training Machine Learning models, enabling accurate predictions. Explore techniques and tools for data labelling.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.pickl.ai\/blog\/guide-to-data-labelling\/\" \/>\n<meta property=\"og:site_name\" content=\"Pickl.AI\" \/>\n<meta property=\"article:published_time\" content=\"2023-08-07T12:20:44+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-08-07T04:56:13+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-7.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Asmita Kar, Hardik Agrawal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Asmita Kar\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"11 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/guide-to-data-labelling\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/guide-to-data-labelling\\\/\"},\"author\":{\"name\":\"Asmita Kar\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/#\\\/schema\\\/person\\\/deb3008b208be14f6776365a3e3bdbf9\"},\"headline\":\"A Comprehensive Guide to Data Labelling\",\"datePublished\":\"2023-08-07T12:20:44+00:00\",\"dateModified\":\"2024-08-07T04:56:13+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/guide-to-data-labelling\\\/\"},\"wordCount\":2158,\"image\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/guide-to-data-labelling\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/wp-content\\\/uploads\\\/2023\\\/08\\\/image3-7.jpg\",\"keywords\":[\"data labeling\",\"data labeling tool\",\"how to add data labels in google sheets\",\"labeled data in machine learning\",\"labelled and unlabelled data\",\"which approach is used for automatic labelling\"],\"articleSection\":[\"Machine Learning\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/guide-to-data-labelling\\\/\",\"url\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/guide-to-data-labelling\\\/\",\"name\":\"Unleashing the Power of Data Labelling in Machine Learning\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/guide-to-data-labelling\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/guide-to-data-labelling\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/wp-content\\\/uploads\\\/2023\\\/08\\\/image3-7.jpg\",\"datePublished\":\"2023-08-07T12:20:44+00:00\",\"dateModified\":\"2024-08-07T04:56:13+00:00\",\"author\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/#\\\/schema\\\/person\\\/deb3008b208be14f6776365a3e3bdbf9\"},\"description\":\"Data labelling is for training Machine Learning models, enabling accurate predictions. 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