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When you are building a machine learning or AI model that acts like a human being, it goes without saying that training data will be your best friend. For your model to make decisions and take action, it first needs to be trained to grasp specific information. Data annotation categorizes and labels data for artificial intelligence applications. When training a machine, you should ensure that the data is properly annotated and categorized in order to meet your specific purpose.
It is for this reason that a robust solution – one that is capable of handling large volumes of data, like the data engine pioneered by Dataloop.ai – will prove essential to your task. These engines represent a one-stop shop that offers a multifaceted platform for data annotation, data management, and production pipelines for various industries – from retails to robotics, drones, autonomous vehicles, media and content, and precision agriculture.
Defining a data annotation tool
A data annotation tool can either be a containerized software, on-premise, or cloud-based solution. You use it to annotate or add labels to production-grade training data when you’re doing machine learning. With a cloud-based tool, your training data is secure as it is stored in the cloud. Team members easily access the data and collaborate. The team members annotate the data in real-time.
On-premise means that the annotation tool is used within the business premises. This is a preferred option because of data security. It also allows team members to respond quickly if there are issues. An on-premise annotation tool needs a license to use. On the other hand, some enterprises prefer the portability of a containerized software solution, as they can move from one environment to another.
What You Should Know About Data Annotation Tools
The most important things to consider are the core features of any data annotation tool. For most requirements, it should have the following:
- Annotation methods. This refers to the capabilities and methods to label your data, and your choice of platform will depend on your existing and future needs. Common capabilities include building and managing guidelines such as label-specific annotation types, attributes, classes, and maps. More advanced features include auto-labeling, which can either assist the annotators to improve their data labeling skills or automatically annotate the data with or without human intervention. But there could be errors, edge cases, and exceptions with automated annotations. Therefore, it is crucial to include a human-in-the-loop approach to ensure exceptional handling and quality control.
- Dataset management. You must have a comprehensive methodology with which to manage the dataset you will annotate. Choose a tool that will capably import and support the high volume of file types and data you should label. The tool should include searching, filtering, sorting, coning, and merging the data sets.
- Data quality control. The data annotation tool you should get much have an embedded quality control within the annotation process itself. There should be real-time feedback and issue tracking while you are doing the annotation.
- Workforce management. Any data annotation tool is going to be used by humans. Even if the tools have AI-based automation features, you will need humans to take care of exceptions and quality assurance.
- Data security is a top priority. The tool should have built-in viewing rights only to data assigned to the annotator. The tool should also be able to prevent the download of data.
When you know the fundamental features of the annotation tool, you will be able to choose the right one.