The data labeling tools you use to train and deploy machine learning models with rich data can make or break your AI project. Can your tools create high-performance models to solve?

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The data annotation tool ecosystem is changing rapidly as more providers offer options for an increasingly diverse set of use cases. Tool improvements are made on a monthly, sometimes weekly basis. These changes bring new tool improvements to existing tools and emerging use cases.

New tools, more advanced features, and changes to options such as storage and security further complicate your tool selection. And, an increasingly competitive market makes it challenging to discern hype from real value.

We call this an evolving guide because we’ll be updating it regularly to reflect changes in the data highlighter ecosystem. So be sure to check back regularly for new information and you can bookmark this page.


In this guide, we’ll introduce data annotation tools for computer vision and NLP (natural language processing) for supervised learning.

First, we’ll explain the concept of Data Highlighter in more detail, introduce you to key terms and concepts, and give you considerations for choosing a tool.

Basic knowledge:

Data Annotation Tools and Machine Learning

What is Data Labeling?

In machine learning, data labeling is the process of labeling data to show what you want your machine learning model to predict. You are labeling (labeling, transcribing, or processing) a dataset with features that you want your machine learning system to learn to recognize. After deploying the model, you want it to be able to recognize these features on its own and make a decision or take some action.

Labeled data reveals features that will train your algorithm to recognize the same features in unlabeled data. Data annotation is used for supervised learning and hybrid or semi-supervised machine learning models involving supervised learning.

What is Data Highlighter?

A data labeler is a cloud-based, on-premises, or containerized software solution that can be used to label production-grade training data for machine learning. While some organizations take a do-it-yourself approach and build their own tools, there are many data annotation tools available as open source or free software.

They are also commercially available for lease and purchase. Data annotation tools are usually designed to work with specific types of data, such as image, video, text, audio, spreadsheet, or sensor data.

6 major functions of data labeling tool machine

1) Dataset management

Labeling begins and ends with a comprehensive way of managing the datasets you plan to label. As a critical part of your workflow, you need to ensure that the tools you are considering can actually import and support the vast amount of data and file types you need to label. This includes searching, filtering, sorting, cloning and merging of datasets.

Different tools can save the output of annotations in different ways, so you need to make sure that the tool can meet your team’s output requirements. Finally, your label data has to be stored somewhere. Most tools will support local and network storage, but cloud storage—especially your preferred cloud provider—may be affected, so make sure your file storage target is supported.

2) Annotation method

This is clearly the core functionality of Data Highlighter – the methods and capabilities to apply labels to data. But not all tools are created equal in this regard. Many tools are strictly optimized to focus on specific types of labels, while others offer a broad portfolio of tools to support various types of use cases.

Almost all provide some type of data or document classification to guide you in how to identify and sort the data. Depending on your current and anticipated future needs, you may wish to focus on specialists or use a more general platform. Common types of labeling functionality provided by data labelers include building and managing ontologies or guidelines, such as label maps, classes, attributes, and specific label types.

Here are just a few examples:

 Image or video: bounding boxes, polygons, polylines, classification, 2-D and 3-D points, or segmentation (semantic or instance), tracking, transcription, interpolation, or transcription.

 Text: transcription, sentiment analysis, network entity relationship (NER), part of speech (POS), dependency resolution or coreference resolution.

 Audio: audio tags, audio to text, tags, time tags

An emerging feature in many data annotation tools is automation or auto-tagging. Using artificial intelligence, many tools will help your human annotators improve their annotations (for example, automatically convert four-point bounding boxes to polygons), and even automatically annotate your data without human intervention. Additionally, some tools can learn from the actions taken by human annotators to improve the accuracy of automatic labeling.

Some labeling tasks are already suitable for automation. For example, if you label images with pre-annotation, a team of data labelers can determine whether to resize or remove bounding boxes. For teams that need to annotate images at the pixel-level segmentation, this can shorten the process time. Nonetheless, there will always be anomalies, edge cases, and errors in automated labeling, so it is critical to include a human-in-the-loop approach for quality control and exception handling.


3) Data quality control 

Your machine learning and AI models will only perform as well as your data. Data annotation tools can help manage quality control (QC) and validation processes. Ideally, the tool will embed QC in the labeling process.

For example, real-time feedback and enabling issue tracking during callouts are important. Additionally, workflow processes such as token consensus can be supported. Many tools will provide quality dashboards to help managers view and track quality issues and assign QC tasks to core labeling teams or dedicated QC teams.


4) Workforce Management

Every data annotation tool is designed to be used by a human workforce—even those that may feature AI-based automation. As mentioned, you still need human hands to handle exceptions and quality assurance. As a result, leading tools will offer workforce management features such as task assignment and productivity analytics, measuring time spent on each task or subtask.

Your data labeling labor provider may use their own technology to analyze data related to quality work. They may use technologies such as webcams, screenshots, inactivity timers, and clickstream data to determine how they support staff to provide high-quality data annotation.

Most importantly, your employees must be able to use and learn the tools you plan to use. Additionally, your labor provider should be able to monitor employee performance as well as work quality and accuracy. It’s even better when they give you direct visibility into the productivity of your outsourced workforce and the quality of the work being performed, such as a dashboard view.



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