Algorithms guide our daily lives today. Artificial intelligence and machine learning algorithms can filter even the simplest judgments, such as a GPS app’s estimated time of arrival or the next music in the streaming queue. We rely on these algorithms for a variety of reasons, including personalization and efficiency.
Nonetheless, their capacity to keep these promises depends on data annotation tools. After all, data annotation involves the process of categorizing information in order to train artificial intelligence to make future judgments.
What is Data Annotation?
Computers can’t interpret visual information in the same way that human brains do. To decide, a computer must be told what it is interpreting and given context.
As the workhorse of our algorithm-driven world, data annotation is responsible for this relationship. It is the human-led activity of categorizing content such as text, audio, photos, and video. By doing so, machine learning models can recognize the content and make predictions.
Data Annotation in Machine Learning
AI and machine learning are two of the fastest-emerging technologies, bringing incredible breakthroughs to a variety of areas around the world. A massive number of training data sets are necessary to construct such automated applications or computers.
Data is the driving force behind machine learning attempts. The more data you have, the more accurate your outcome will be. However, having raw data is insufficient. This data must be annotated so that machine learning models can distinguish items in an image, comprehend human speech, and perform a range of other tasks.
Before releasing a product, its precision should be improved. Humans will have to manually analyze each image to assess whether the quality of the annotations is good enough to teach the algorithms.
Humans may see a correlation between correctly annotated data and project success even on the surface. The significance of data annotation stems from the fact that even little inaccuracies can have serious effects. Humans have an advantage over machines in this field because they can see through ambiguity, comprehend intent, and a variety of other elements associated with data annotation.
Best AI Data Annotation Tools
A data annotation tool is a software solution that may annotate production-grade training data for machine learning. It can be cloud-based, on-premises, or containerized. While some businesses prefer to construct their own tools, there are various open source or freeware data annotation solutions accessible.
Annotation tools for data are typically built to work with certain forms of data, such as image, video, text, audio, spreadsheet, or sensor data. Here’s a closer look at some data annotation tools we think are among the best on the market right now.
V7 is an automated annotation platform that combines dataset management, image and video annotation, and autoML model training to perform labeling tasks automatically. V7 allows teams to store, manage, annotate, and automate data annotation operations in pictures, video, medical data, microscopy images, PDF and document processing, and other formats.
Labelbox’s training data platform is designed to assist you in improving your training data iteration loop. It’s built around three main pillars: the ability to annotate data, diagnose model performance, and prioritize tasks based on your findings. With Labelbox, you can minimize annotation expenses by 50-80%, iterate 3 times faster on your AI data to build more effective models, and collaborate more efficiently with data scientists, labelers, and domain experts.
3. Scale AI
Scale is a data platform that enables annotations of large volumes of 3D sensor, image, and video data. Scale provides ML-powered pre-labeling, an automated quality assurance system, dataset administration, document processing, and AI-assisted data annotation for autonomous driving.
This data annotation tool supports numerous data formats and can be used for a range of computer vision applications, such as object detection, classification, and text recognition.
Superannotate is an image and video annotation tool that automates and streamlines computer vision activities. It enables the creation of high-quality training datasets for a variety of computer vision applications, such as object detection, instance and semantic segmentation, keypoint annotation, cuboid annotation, and video tracking.
Vector annotations (boxes, polygons, lines, ellipses, keypoints, and cuboids) and pixel-wise annotation using a brush are among the techniques available.
Dataloop supports the entire AI lifecycle, including annotation, model evaluation, and model refinement, by including human feedback into the loop.
It includes tools for doing fundamental computer vision tasks, such as detection, classification, key point identification, and segmentation. Dataloop can handle both picture and video data.
How to Choose a Data Annotation Tool?
There are several factors to consider when deciding whether to build or buy. If, after examining all the considerations, you conclude that the effort and expense of a DIY method is not worth the potential gain of customizing and maintaining IP, the next decision you must make is which commercial solution to acquire. In this part, we will look at some of those issues.
1. What is your use case?
First and foremost, the sort of data you want to annotate, as well as your business processes for conducting the task, will determine your tool selection. There are tools for labeling text, images, and videos. Some image labeling software can also classify videos.
2. How will you handle quality assurance requirements?
A key aspect of your data annotation tool is how you want to measure and control quality. Many commercially available tools include built-in quality control (QC) features that allow you to examine, provide feedback, and fix activities. These might include: consensus, gold standard, sample review, and Intersection over Union (IoU).
3. Who will be using the tool?
The workforce is an often-overlooked factor of tool selection. Whether your data is annotated by employees or contractors, crowdsourcing, or an outsourced provider, your workforce will require access to and training on your data annotation platform, with precise task instructions tailored to your use case.
4. Do you need a partner?
The provider from which you purchase the tool is just as important as the product. Consider how easy it is to do business with the company that provides the tool, as well as their willingness to collaborate. AI development is an iterative process that necessitates changes along the way. Is it possible for them to consider input or ideas for additional features for their tool that would make your work easier or your AI models operate cleaner and produce better results? Find a partner who is prepared to collaborate on such issues, rather than a vendor who just provides a tool.
Finally, keep in mind that your annotation tasks will most likely develop with time. Each machine learning to model challenge is unique. The set of instructions you use today to gather, clean, and annotate your data may change in the following weeks, if not days. Anticipating those changes is beneficial, and you should keep this in mind while deciding on a data annotation tool and the workforce that will use it to label your data.
Importance of Data Annotation to Machine Learning and AI
Proper application of data annotation is only achievable when a perfect combination of human intelligence and smart tools is used to generate high-quality training data sets for machine learning. According to the MIT Technology Review Report, the most difficult hurdle to deploying AI has been properly annotated data. Enterprises should invest in robust data annotation capabilities to enable AI and ML model development and avoid costly failures. As humans, we have an advantage over computers because we can deal with ambiguity, decipher intent, and a variety of other factors that influence data annotation.
Accurately annotated data affects whether you build a high-performing AI/ML model to solve a complicated business problem or spend time and money on a failed experiment. When time and resources are limited to develop such capabilities, consulting data annotation businesses is a wise decision. Aside from saving time and money, data annotation professionals enable you to swiftly develop your AI capabilities and conceptualize machine learning solutions to satisfy market demands and client expectations.
Outsource AI Data Annotation Services for Machine Learning
Algorithms will continue to affect consumer experiences for the near future. However, algorithms can be imperfect and suffer from the same flaws as their developers. To ensure that AI-powered experiences are efficient and effective, data annotation must be done by teams with a sophisticated grasp of what they’re annotating. Only then can we assure that data-driven solutions are as accurate and representative as feasible.
Are you in need of data annotation services? We’ve got your back. Contact us today!