What is data annotation?

You’re marking up data with the properties you want the ml model to understand by classifying, labeling, extracting, or analyzing it. When your ML model is up and running, your goal is to be able to detect properties on its own and then to make a judgment or take action based on them.

  • The practice of labeling data to show the outcome you want your machine learning model to predict is known as data annotation in machine learning.

Annotated data shows traits that will teach your algorithms to recognize those same properties in unannotated data.

Data annotation tools and properties

While some companies prefer to create their own tools, there are a plethora of open source and freeware data annotation solutions accessible.

  • An annotation tool is a solution that may be used to add notes in training data for ml.

It can be:

  1. cloud-based,
  2. on-premise or
  3. containerized.

Annotation tools are all intended to work with certain forms of data. SaaS (cloud), Kubernetes, and on-premise are among the deployment types available.

When it comes to data annotation features you should always look at those who are the most important. Quality control, method, management, and security.

  • Quality control – The quality of your data will determine how well your machine learning and AI models perform. Quality control (QC) and verification processes can be made easier with data annotation tools. Ideally, the quality control is the part of the tool process.

Real-time feedback and launching problem tracking during annotation, for example, are critical. Workflow methods such as labeling consensus might also be enabled. Many systems provide a quality dashboard that allows managers to see and track quality concerns while also delegating QC duties to the main annotation team or a specialist QC team.

  • Method – The techniques and possibilities are probably the most important element of data annotation tools. In this sense, however, not all instruments are made equal. Many solutions are tailored to certain sorts of problems, while others provide a diverse set of capabilities to support a variety of cases.

Almost all of them include some sort of document classification to help you identify and organize your information. You may want to focus on experts or go with a generic platform, depending on your present and future needs.

Building and managing classes, properties, and particular annotation kinds are typical types of annotation capabilities.

Automation, or auto-labeling, is a new property in many data annotation technologies. Many AI-powered technologies can help your human labelers improve their annotations or annotate your data without the need for human intervention. Furthermore, certain technologies may learn from your human activities to enhance accuracy.

  • Management – Annotation starts and finishes with a system for managing the dataset you want to annotate. You must guarantee that the tool you are contemplating will really import and handle the large volume of data and file formats you need to label as a vital component of your process. Datasets may be searched, filtered, sorted, cloned, and merged using this method.

Because various tools preserve annotation output in different ways, you’ll want to be sure the tool you choose will match your team’s output needs. Finally, you’ll need a place to save your annotated data. Although most programs support local and network storage, cloud storage – particularly from your favorite cloud vendor – can be hit or miss, so double-check that your file storage objectives are supported.

  • Security – You want to be sure that your data is safe, whether you’re annotating valuable intellectual properties of sensitive protected personal information. Tools should prohibit data downloads and limit an annotator’s viewing privileges to data not allocated to her. A data annotation tool may provide secure file access depending on whether it is implemented in the cloud.

Most tools will additionally maintain a record of data, such as author, time, and date, for use cases that fall within regulatory compliance requirements. If you’re required to follow various rules (SOC1, SOC2, SSAE, or any other), you’ll want to be sure your annotation tool makes sure you stay compliant.

There weren’t a plethora of data annotation tools to buy only a decade ago. If they wanted to utilize AI to solve a severe business problem or produce a revolutionary product, most early adopters had to rely on open source.

A slew of data annotation tools appeared a few years ago, each giving featured and completed commercial data labeling solutions. The appearance of professionally built tools prompted a debate among AI project teams over whether they should continue to DIY and buy or build their own tools.