Best Data Labeling Software
Data labeling software are an artificial intelligence tools that supervises data management, training data, model versioning, data sourcing, data annotation, quality control, and model production for data science and machine learning teams. These tools source, manage, label, train, and classify unstructured data such as texts, videos, images, audio, or PDF into labeled datasets to create efficient training data pipelines.
Data labeling, also known as data annotation tools or data tagging, is a building block for an AI development lifecycle for businesses. Businesses deploy data labeling software for industry-based applications like ML model generation, fine-tuning large language models (LLM), evaluating LLMs, computer vision, image segmentation, API calls, object detection, and tracking, named entity recognition, OCR, and text recognition. These AI models reduce the classification challenges for data science and machine learning teams and improve AI data management workflows to build efficient machine learning products.
Businesses use data labeling tools to label text data, audio files, images, and videos and gather real-time feedback from customers, stakeholders, and decision-makers to upgrade products. These tools are also used for sentimental analysis, question answering, speech recognition, and content generation. Data labeling tools can be integrated with generative AI software, project management software, MLOPs platforms, data science and machine learning platforms, LLM software, and active learning tools to label data, pre-train models, assure quality control, and operationalize ML production.
Additionally, these products provide security, provisioning, and governing capabilities to ensure only those authorized to make version changes or deployment adjustments can do so. These data labeling tools can differ in what part of the machine learning journey or workflow they focus on, including explainability, model testing, model validation, feature engineering, model risk, model selection, model monitoring, and experiment tracking. The ultimate goal of a data labeling platform is to build agile, precise, and cost-effective data training pipelines to enhance model response accuracy.
To qualify for inclusion in the Data Labeling category, a product must:
Best Data Labeling Software At A Glance
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SuperAnnotate is the only fully customizable, one-stop platform for building exactly the annotation tools and workflows your AI projects demand—while unifying the management of all your teams, vendors
- Student
- Information Technology and Services
- Computer Software
- 60% Small-Business
- 26% Mid-Market
- Reviewers find SuperAnnotate’s platform easy to use.
- Reviewers like the support provided by the product’s support team.
- Reviewers wish that the product had more integrations.
535 Twitter followers
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- User Satisfaction
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Appen collects and labels images, text, speech, audio, video, and other data to create training data used to build and continuously improve the world’s most innovative artificial intelligence systems.
- Information Technology and Services
- 55% Small-Business
- 28% Mid-Market
- Overview
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- User Satisfaction
- Seller Details
Dataloop is a cutting-edge AI Development Platform that's transforming the way organizations build AI applications. Our platform is meticulously crafted to cater to developers at the heart of the AI d
- Computer Software
- Information Technology and Services
- 39% Mid-Market
- 32% Small-Business
- Overview
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- User Satisfaction
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Encord is the multimodal data management platform for AI. With Encord, AI teams can easily manage, curate, and label images, videos, audio, documents, text, and DICOM files on one unified platform whi
- Computer Software
- Hospital & Health Care
- 52% Small-Business
- 40% Mid-Market
599 Twitter followers
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- User Satisfaction
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Sama is a globally recognized leader in data annotation solutions for enterprise computer vision and generative AI models that require the highest accuracy. As an industry pioneer with 15 years of exp
- 55% Small-Business
- 36% Enterprise
233,723 Twitter followers
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- User Satisfaction
- Seller Details
Datature is an AI Vision platform that simplifies computer vision development by unifying data labeling, model training, and deployment into a single workflow. By eliminating the need for fragmented t
- Computer Software
- 61% Small-Business
- 29% Enterprise
158 Twitter followers
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- What G2 Users Think
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V7 is a powerful AI training data platform that enables you to annotate images, videos, documents, and medical imaging files. It is the quickest way to obtain high-quality annotated data for training
- Information Technology and Services
- Computer Software
- 55% Small-Business
- 36% Mid-Market
- Reviewers like V7's intuitive UI, considering it to be user-friendly.
- Reviewers of the software reported some problems with bounding box capabilities.
- Reviewers have appreciated that one can go live quickly with the product.
3,241 Twitter followers
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Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to inject
- Computer Software
- Information Technology and Services
- 56% Small-Business
- 31% Mid-Market
2,521 Twitter followers
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Labellerr is a computer vision workflow automation platform. It helps ML teams to manage their AI development lifecycle much more efficiently. It helps teams to collaboratively work on data labeling
- Information Technology and Services
- 57% Small-Business
- 38% Mid-Market
68 Twitter followers
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Clarifai is a leader in AI orchestration and development, helping organizations, teams, and developers build, deploy, orchestrate, and operationalize AI at scale. Clarifai’s cutting-edge AI workflow o
- Computer Software
- Information Technology and Services
- 59% Small-Business
- 27% Mid-Market
10,976 Twitter followers
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BasicAI Cloud (https://app.basic.ai) is an All-in-One Smart Data Annotation Platform with strong multimodal feature and AI-powered annotation tools that supports: - Auto-annotation and objects tracki
- Information Technology and Services
- Computer Software
- 44% Small-Business
- 31% Mid-Market
84 Twitter followers
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We are a data labeling company that focuses on providing high quality annotation services and excellent customer support. We are the best choice for: Image Annotation Video Annotation Data validatio
- Computer Software
- 56% Small-Business
- 23% Mid-Market
348 Twitter followers
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Company Overview: CVAT.ai is a global provider of data annotation tools and services, known for developing one of the most popular open-source annotation tools, CVAT. In addition to the open-source p
- 50% Small-Business
- 29% Mid-Market
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- Seller Details
Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning quickly. SageMaker Ground Truth offers easy access to public and private human labelers and provide
- Information Technology and Services
- 37% Enterprise
- 37% Small-Business
2,230,610 Twitter followers
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Kili Technology is a comprehensive labeling tool where you can label your training data fast, find and fix issues in your dataset, and simplify your labeling operations. Kili Technology dramatically a
- Information Technology and Services
- Computer Software
- 39% Mid-Market
- 37% Small-Business
432 Twitter followers
Learn More About Data Labeling Software
What is Data Labeling Software?
Data labeling software labels or annotates data for training machine learning models. Machine learning algorithms rely on large amounts of labeled data to learn patterns and make predictions. Data labeling solutions help humans identify and label the relevant features and characteristics of the data that will be used to train the machine learning model.
Many types of data labeling solutions are available, ranging from simple tools that allow users to label data manually to more advanced tools that use machine learning algorithms to automate the labeling process. Some data labeling software also includes features such as image annotation tools, which allow users to label and annotate images and other visual data.
Data labeling software is used in various applications, including natural language processing, image and video classification, and object detection. It is an important tool in the development and training of machine learning models and plays a critical role in their accuracy and effectiveness.
What types of data labeling software exist?
Selecting a data labeling software requires a prior evaluation and understanding of data-driven workflows in your business. Below are the types of software you can consider.
- Manual labeling software: These data labeling platforms segment, label, and classify data with the help of a "human in the loop" service. Human annotators label the training data based on businesses' geographic locations. The data annotation service is extended to the ML model development workflow, and labeling data becomes more effective.
- Automated labeling software: The automated data labeling software preprocesses raw datasets consisting of text, images, liDAR data, DICOM, PDF, or audio using an unsupervised learning approach. The algorithm assigns labels and categories to data without referring to external annotators.
- Active learning labeling software: Also known as active learning tools, these are semi-supervised tools that follow a "query-based" approach to labeling data. Based on the uncertainty score, they query data using manual or annotator labeling. For more challenging labels, they prompt the human annotator with queries.
- Crowdsource labeling software: These data labeling platforms crowd data labeling services to a crowd of developers to train high-quality data pipelines. Custom data labeling can be ideal for large or enterprise-sized teams.
- Integrated labeling and model training software: These tools provide combined services for data labeling and predictive modeling. Using advanced data analysis, users can label, train, and build machine learning models to optimize their production cycles.
What are the Common Features of Data Labeling Software?
There are several features that are often included in data labeling software, including:
- Label assignment: Data labeling software allows users to assign labels or tags to specific data points, such as text, images, or videos.
- Annotation tools: Some data labeling software includes tools for annotating data, such as bounding boxes, polygon drawing tools, cloud points, keymakers, and point annotation tools. These tools can be used to highlight specific features or characteristics of the data.
- Machine learning algorithms: Some data labeling software uses machine learning algorithms to automate the labeling process or generate initial labels for data, which humans can then review and correct as needed.
- Data management and organization: Data labeling software often includes features for organizing and managing large datasets, such as the ability to filter and search for specific data points, track progress and completion, and generate reports.
- Collaboration tools: Some data labeling software includes collaboration tools, such as the ability to assign tasks to multiple users, track changes and revisions, and review and discuss data labeling decisions.
- Integration with data science and machine learning platforms: Some data labeling software is designed to integrate with popular data science and machine learning platforms, such as TensorFlow or PyTorch, making it easier to use the labeled data to train machine learning models.
- Image, text, audio, or video annotation: These tools comply with multiple unstructured data formats to train and validate models designed to generate output in images, text, video, audio, PDF, and so on.
Benefits of Data Labeling Software
Choosing a data labeling platform empowers businesses to either pre-train existing machine learning models to save time or build new models to upgrade their workflows and train teams.
While data labeling platforms can help do both, it also has some significant benefits listed as under:
- Improved accuracy and quality of labeled data: Data labeling software can help ensure that data is accurately and consistently labeled, which is critical for the accuracy and effectiveness of machine learning models.
- Increased efficiency and productivity: Data labeling software can help streamline the data labeling process, allowing users to label more data in less time. This can be particularly useful for large datasets or repetitive or routine tasks.
- Enhanced collaboration and team communication: Some data labeling software includes collaboration tools, such as the ability to assign tasks to multiple users and track changes and revisions. These tools can help improve communication and coordination within teams working on data labeling projects.
- Reduced cost: Using data labeling software can help reduce the cost of data labeling projects by automating routine tasks and reducing the need for manual labor.
- Increased flexibility and scalability: Data labeling software can be used to label a wide variety of data types and can be easily scaled up or down as needed to meet project demands.
- Respite for data operations, ML, and data science teams: These solutions offer agile service marketplaces with high-quality labelers and annotators that solve the problems of data cleaning, preprocessing, and classification for these teams.
- Superpixel segmentation and brushes: These tools are also widely used for image recognition, natural language processing (NLP), and computer vision algorithms. It creates region pools using brushing and superpixel segmentation to classify images.
Who Uses Data Labeling Software?
The data labeling tools are a must-have for businesses that want to foray into AI automation and build robust and efficient product applications and SDK with pre-installed machine learning capabilities.
Below are the individuals and organizations that use data labeling platforms:
- Data scientists and machine learning engineers: Data scientists and machine learning engineers use data labeling software to label and annotate data that will be used to train machine learning models. This helps the models learn to recognize patterns and make predictions based on the labeled data.
- Business analysts and data analysts: Business analysts and data analysts may use data labeling software to label and annotate data to create reports and visualizations or for use in machine learning models.
- Quality assurance professionals: Quality assurance professionals may use data labeling software to label and annotate data to test and debug machine learning models or other software applications.
- Researchers: Researchers in various fields, such as computer science, linguistics, and biology, may use data labeling software to label and annotate data to conduct research or develop machine learning models.
Alternatives to data labeling software
Some alternatives to data labeling software provide annotation and labeling services along with other machine learning features.
- Natural language processing (NLP) software: The NLP software derives semantic relationships between words of an input sentence and generates relevant and personalized content. These tools replicate the functioning of a human brain to register prompt intent and derive coherent content blocks.
- Machine learning operationalization (MLOps software): The MLOPs software facilitates the entire machine learning model journey, from data preprocessing to ML integration and delivery. It applies various DevOps automation concepts and runs ML-based workflows without human supervision.
- Image recognition software: Image recognition software detects, categorizes, and localizes digital images or photographs. It is based on specialized deep-learning models that group data into grids and identify relevant categories of all objects.
Challenges with Data Labeling Software
Even though data labeling software reduces costs, provides security and privacy to data, and moderates data quality control, some evident challenges can occur at any stage of working with this platform.
Below are some of the challenges of data labeling software
- Data quality and consistency: It is not certain that data labeling tools would predict accurate labels for ML models. Sometimes, the platform can incorrectly categorize text as video or process incorrect calculations, which can lower the data quality.
- Scalability: As a business receives large influxes of data, repurposing raw data to train models, make model versions, calculate risks, and be consistent with quality control becomes a challenge and results in scalability problems for different teams across the company.
- Cost: Though data labeling platforms tend to be cheaper than other expensive human annotation services, submitting a large cluster of datasets for categorization can become costly. It would exhaust your credits and leave you with no alternative but to upgrade to a more expensive plan.
- Complexity of tasks: Not all data labeling tasks are simple. Some require deep domain exercises and more specialized algorithm training, such as reinforcement learning, query sampling, or entropy, to build ML models accurately without investing in external annotation services.
- Data privacy and security: These platforms are open source or paid. However, they retrieve and store data on hybrid or public cloud storage platforms, which can infect your dataset and give hackers and fishers leeway to infect the data.
What companies should buy data labeling software?
Companies that want to optimize the quality of their datasets and build powerful algorithms should consider data labeling software. Not just because it helps label data but because it can build accurate predictions and forecasts. Here are some companies that can benefit from these tools:
- Machine learning startups or research labs: These companies conduct the majority of machine learning experiments and constantly work with data tools. Investing in a data labeling tool can benefit their AI research and ML model development processes.
- Data companies: Companies that provide data management services like search engines, e-commerce platforms, or social media management tools also need data labeling software to generate effective algorithms that generate accurate responses and deal with large data volumes.
- Market research companies: Companies that conduct market research or gather customer insights and trends can also benefit from data labeling platforms. These platforms allow them to gather real-time market trends and track consumer behaviors.
- Healthcare organizations: These companies utilize data labeling platforms for early detection of diseases, medical imaging, patient recordkeeping, consultation, and treatments. With this software, they accurately study patient data and forecast treatment cycles.
How to Buy Data Labeling Software
Investing in data labeling software is a step-by-step process that requires the input of all related teams and stakeholders. Below are the steps buyers need to follow chronologically to purchase the best data labeling platform for their business.
Requirements Gathering (RFI/RFP) for Data Labeling Software
Before purchasing, buyers should consider their needs and determine what they hope to achieve with this software. Evaluate the type of database system, products, AI maturity, and budget data from revenue teams. Also, make a list of the data-related and language services you expect from the product. Enlist all these points in the form of a structured request for proposal (RFP) and get the approval of your teams and stakeholders who are involved in the decision-making process.
Compare Data Labeling Software Products
Evaluate the shortlisted products' features, security and privacy guidelines, pros and cons, pricing, and AI functionalities. Compare the features and benefits with the requirements your team has listed in the request for proposal. Analyze the budget, contract metrics, and return on investment for each software feature and compare them with those of other contenders in the market.
At this stage, buyers can also request demos or free trials to see how the software works and ensure it meets their needs. While shortlisting vendors, it is also crucial to consider their credibility. Look for vendors with a strong track record and a good reputation.
Selection of Data Labeling Software
Discuss all shortlisted software's technical and configuration workflows with your IT and software development teams. Sit with them to analyze current software consumption, active subscription plans, system of records, and IT audit reports, and then check where this software fits in your tech stack. Discuss the compatibility of the software with related account executives and sales teams to ensure that the software doesn't cause more overheads and storage expenses for your teams.
Negotiation
After finalizing the software, get your legal teams to draft a legitimate contract outlining RFP terms, renewal policies, data retention and privacy policies, and the vendor's non-compete and discuss it with the vendor. At this stage, it is also feasible to negotiate for a better subscription rate, more features, or add-ons that buyers are interested in at the vendor's discretion.
Final decision
The final decision to purchase data labeling software lies with the buyer's decision-making teams. These could be the chief information officer (CIO), head of the data science team, or procurement team. While making this decision, it is also important to consider budget constraints, team queries, or business objectives. It will be helpful to consult with stakeholders and experts, like data scientists and ML engineers, to get their input on the best data labeling solution for the institution.
What does data labeling software cost?
The cost of data labeling software can vary widely depending on its specific features and capabilities, as well as the size and scope of the deployment. Some software is free or open-source, while others are commercial products sold on a subscription or per-use basis.
Data labeling software designed for enterprise-level use with a wide range of advanced features will be more expensive than straightforward solutions. Prices can range from a few hundred dollars per year for an introductory subscription to several thousand dollars for a more comprehensive solution.
It is essential to evaluate subscription, license, pay-per-seat, and pay-per-token usage costs to check whether the product is suitable for your business and has scope for a decent return on investment (ROI). While you are engaged in the monetary calculations, factor in software upgrade cost, business size, version, software maintenance, and upsell costs to indicate the budget clearly. These tools can help improve productivity and efficiency, contributing to ROI calculation.
To calculate the ROI of data labeling software, the following formula can be used:
ROI = (Benefits - Costs) / Costs
"Benefits" is the value of the time saved and increased productivity resulting from using the software, and "Costs" is the total cost of the software license and any additional costs associated with implementation and use.
Implementation of data labeling software
When considering purchasing data labeling software, companies should have a rough vision of how to implement it for data science and machine learning teams.
Other factors, such as alignment with notebook editors, statistical tools, data analysis limitations, training, and testing ML cycles, will be altered and modified per the implementation timeline of data labeling software. Below are some tips to ensure a smooth implementation.
- Integration with existing data and ML workflows: Consult your software development teams on setting up user permissions and integrating this platform with your existing code development platform, such as R or Python editors. The first step is to ensure it is compatible with various data formats, data types, data analysis tools, and other collaborative ML tools.
- Customization and flexibility in labeling tasks: These platforms must be agile and compatible with datasets of multiple formats and languages. It should provide customization for various tasks such as image recognition, computer vision, audio generation, video generation, and speech recognition. Labeling unstructured data should be open to anyone who authenticates their identity through multi-factor authentication and is an authorized user.
- Collaboration and workforce management features: The data labeling platform needs to be activated for model prototype and version control. It should have features like role-based access control, data privacy and security guidelines, user authentication, model collaboration, and ML code supervision. The platform should be accessible to respective team members so they can double-check the labeled tasks and stop the model from hallucinating at any stage of the training data pipeline.
- Quality assurance and review mechanisms: When a model's output accuracy depends on the quality of training data, it is evident that data labeling platforms need to be set of modulation accuracy, quality control, and labeling review mechanisms. Given the models might inaccurately label datasets or predict wrong values, the labels need to be further supervised by a human in the loop service or external human oracle.
- Scalability, automation, and cost efficiency: As labeling needs grow, ML engineers and developers need to invest in a scalable and cost-efficient data labeling solution that doesn't obstruct their network infrastructure and database architecture. The final implementation step is to ensure that the controls are set, the license is active, and the platform is retrieving and labeling data typically.
Data Labeling Software Trends
Overall, these trends reflect the growing importance of data labeling in the machine learning and AI ecosystem and the need for tools and technologies to help organizations create and manage large datasets of labeled data efficiently and effectively. There are several trends surrounding data labeling software that are worth noting:
- Increased adoption of artificial intelligence (AI) and machine learning (ML): One key trend in data labeling software is the increasing adoption of AI and ML technologies. Many software solutions now incorporate AI and machine learning algorithms to automate and streamline the data labeling process, improving efficiency and accuracy. As with general AI software, G2 expects this software to get cheaper.
- Growing demand for high-quality labeled data: Another trend is the growing demand for high-quality labeled data to train and test machine learning models. Data labeling software can help organizations create and manage large datasets of labeled data, improving the quality and reliability of machine learning models.
- Focus on user experience and collaboration: Another trend in data labeling software is a focus on user experience and collaboration. Many data labeling software solutions now offer intuitive and user-friendly interfaces, tools, and features that facilitate collaboration and teamwork.
Researched and written by Matthew Miller
