Data Labeling: Significance, Tools, Skills and Courses

May 16, 2024 5 min read By Cogito Tech. 25 views

Data labeling is one of the preprocessing stages in the development of machine learning models. It involves identifying raw data like images, text files, videos, etc. and then adding a label/s to the data for specifying model context. It allows for accurate predictions by machine learning models and is the key technology behind various machine learning and deep learning applications like computer vision and natural language processing. Proper data labeling is imperative to your AI model’s performance. Though it may sound easy on paper, but in practice it involves selecting the right annotation tools for outlining objects of interest carefully leaving little or no room for error.

What is the difference between Data Labeling and Data Annotation?

Data labeling Vs data annotation

Data labeling and data annotation both focus on enhancing data for machine learning and involve tagging information pieces being fed into a machine learning model. There is a thin line of distinction between data labeling and data annotation and is primarily to do with formats.

Data labeling is focused only on assignment of specific predefined labels to a data point, but annotation involves attaching a lot more information. While data labeling is ideal for categorical or binary classification tasks, data annotation involves a wider range of practices where machine learning algorithms learning more about the entities and their interactions.

How to Select the Best Data Labeling Tool?

High quality training data along with a solid data management infrastructure is necessary for developing an AI model that’s mature, profitable and ready to go to market. As per an IBM report, 24% of global organizations claim “lack of tools or platforms to develop models” and “too much data complexity” are some of the key hindrances in AI adoption.” These obstacles can be overcome by making sure that the data labeling process is part of a central, automated machine learning operations pipeline.

By using an ideal training data toolkit, one can seamlessly integrate with the rest of their tech stack and build AI products in a quick, cost-effective and low risk fashion. Selecting a third party data labeling tool which is ideal for your needs is indeed a challenging exercise given the variety of platforms present in the market. So, in order to assist you in selecting the right data labeling tool, here are ten tips.

Tips for Selecting a Data Labeling Tool

1 Data and types of annotation: Depending on the need of the project, the data labeling tool should be capable of supporting a variety of data formats and annotation types. 6 Security: The tool must offer enterprise grade data security for ensuring safe development and scaling of product to cater to all kinds of global customers including government and enterprise level organizations.
2 Security: The tool must offer enterprise grade data security for ensuring safe development and scaling of product to cater to all kinds of global customers including government and enterprise level organizations. 7 Workforce: Several tools help with recruitment of industry-trained skilled labelers for delivering qualitative outputs.
3 Customer support: The data labeling tool’s support team must respond promptly to bottlenecks to eliminate down times. 8 Data management: A data labeling platform having smart dataset management capabilities not only helps in lowering data volumes and simultaneously creating training sets, but also minimizes annotation costs, time to market, and boosting the quality of your products.
4 Integration of API and SDK: The data labeling tool is a part of your machine learning operations pipeline and should seamlessly integrate with the rest of your tech stack. 9 Model training: A strong data labeling platform helps in proper training and fine-tuning of experimental models.
5 Quality assurance: This assists not only in complying with the most stringent standards but also streamlines the annotation process through centralization of reviews and corrections. 10 Price: This is a key point of comparison among various platforms and also a key factor in taking decisions.

Keeping the above ten points in mind, here are six data labeling platforms that comply with the above criteria.

6 Best Data Labeling Tools

  1. V7 Darwin: It is ideal for solving labeling tasks in a quick and accurate manner. It helps with annotating data in various formats including videos and medical images. It reduces manual labeling time along with ensuring pixel-perfect quality.
  2. Labelbox: This is a well-known AI data engine which assists organizations in managing, curating and analyzing unstructured datasets with the aim of converting them into quality training data using traditional and vector search via active learning.
  3. Scale AI: This is ideal for government and automotive sector and permits one to manage their entire AI management cycle right from data preparation to product deployment.
  4. Dataloop: It enables one to fine-tune foundation models along with customizing large language models and other generative AI models powered by continuous learning, RLHF, etc.
  5. SuperAnnotate: This allows one to label images, videos, audio, text, and LiDAR. It also assists with fine-tuning large language models with RLHF, instruction fine-tuning, image captioning, and more. It offers a toolkit to try out readymade LLM and generative AI templates or create new ones which can be utilized for free.
  6. Encord: This allows one to annotate image and video data for many computer vision tasks. It can support medical DICOM and NIfTI formats, ECG annotation solution, and synthetic-aperture radar (SAR) data. It is a great choice for healthcare, aerospace, and defence professionals.

Who is a data labeler?

A data labeler is a person who annotates and labels datasets so that they are easy to understand and usable for training AI algorithms. The job of a data labeler involves meticulous analysis and categorization of data, identification of patterns, objects, etc. in images, texts or videos. They play a contributory role in the development and training of AI models. They enable machines to recognise and interpret information in an accurate manner. Hence, data labelers are also called AI training specialists.

What skills and qualifications are required for data labeling jobs?

Some of the broad qualifications for becoming a data labeler are; basic computer literacy, keen eye for detail, knowledge of language and culture, and familiarity with labeling software and tools.

1 Course Name:
Practical Crowdsourcing for Efficient Machine Learning

Platform: Coursera
Eligibility: Beginners
Duration: 17 hours
Award: Certificate
Cost: Free.

Description:
It is a free course on the Coursera platform with 11 instructors. It has been designed for teaching learners data labeling that’s effective and scalable for machine learning and different business processes. This course gets enables one to get acquainted with crowdsourcing and learn how to design a crowdsourcing project on your own.
2 Course Name:
Machine Learning Data Lifecycle in Production by DeepLearning.AI

Platform: Coursera
Eligibility: Advanced learners
Duration: 4 weeks
Award: Certificate
Cost: Free.

Description:
It is created to assist with building data pipelines by gathering, cleansing, validating datasets and assessing data quality. The outline of the four-week course is as follows:
Week 1: Learn collecting, labeling and validating data
Week 2: Learn feature engineering, transformation, and selection
Week 3: Understand data journey and storage
Week 4: Learn advanced data labeling methods, data augmentation, and pre-processing different data types.
3 Course Name:
Optimize ML Models and Deploy Human-in-the-Loop Pipelines by DeepLearning.ai and AWS

Platform: Coursera
Eligibility: Advanced
Duration: 14 hours
Award: Certificate
Cost: Free.

Description:
This course focuses on teaching a series of performance-improvement and cost-reduction techniques for automatic tuning of model accuracy, comparison of prediction performance and generation of new training data with human intelligence. It also involves setting up of a human-in-the-loop pipeline for fixing misclassified predictions and generating new training data using Amazon Augmented AI and Amazon SageMaker Ground Truth.
4 Course Name:
Develop Custom Object Detection Models with NVIDIA and Azure Machine Learning

Platform: Microsoft
Eligibility: Advanced
Duration: 2 hours 25 minutes
Award:
Cost: Free.

Description:
The Azure Machine Learning studio is a GUI based integrated development environment to build and operationalize machine learning workflow on Azure. It teaches development of custom object detection models using this service and NVIDIA GPU accelerated virtual machines.
In summary,

Data labelers play a critical role in the AI revolution by producing the fundamental building blocks for training and developing AI systems. A data labeler makes the data understandable for AI models by adding labels. The key skills necessary for this job are computer literacy, attention to detail, language proficiency, and familiarity with labeling tools. Jobs in data labeling are promising with advancements in AI and requires special skills and integration with other industries. All in all, data labelers play a vital role in the growth of AI and poised for achieving success in this growing field.

If you wish to learn more about Cogito’s data annotation services,
please contact our expert.

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