Handling a machine learning project entails converting raw data into usable information for artificial intelligence (AI) systems. The machines can only function according to the parameters defined by specific datasets. On the other hand, the machine cannot read raw data. The way to bridge the gap between data and machine learning is to employ data annotation.
The process of data annotation requires a program and the skills of a human data annotator. The annotator collects raw data and produces labels, categories, and other descriptive elements to help the AI learning machine interpret and act on the supplied information. The annotated raw data used in machine learning and AI are typically made from alphabetic text and numerical data but can also be made from audio/visual elements and photos.
Choosing data annotation tools
Handling data annotation projects sometimes poses the question of whether to buy a robust program or build one that is tailored to a particular project. Thus, it becomes a toss between investing time and resources to build an annotation tool. You can customize or choose a commercial tool created by a professional data labeling company to help you begin the data labeling task at once. In making a choice, there are several factors worth considering.
Your use-case
You should first consider the type of data you will annotate and the business processes you will initiate to handle the work. For example, different tools are needed to label text, video, or image. There are image labeling tools that can also handle video labeling.
More providers of data annotation tools have realized that instead of offering a tool for each task, it would be more efficient to create a holistic platform, such as what is available at https://dataloop.ai/solutions/data-annotation/ for machine learning. If they provide individual annotation tools, each one will only have features that enrich the specific data. In contrast, a data annotation platform offers a better environment that supports the annotation itself and the AI development process.
You can find a platform that includes several features like multiple annotation options such as text, audio, 2D or 3D, quality control workflow, or more storage options such as the cloud, local, or network storage. Other platforms can likewise accept pre-annotated data. Some even include embedded neural networks that you can learn from the manual annotations while using the platform. If your projection indicates that your project will evolve over time, choosing a data annotation platform may give you the flexibility you will need in the future.
Management of quality control requirements
Measuring and controlling data quality and inputs is vital in data annotation. Today’s commercially available tools have quality control features built into the program, including review, feedback, and correction of tasks.
While it is possible to automate part of the quality control process, it is still better to have humans do most of the QC tasks, as you cannot discount the margin of error in individual programs.
Users of annotation tools
Often, the company buys a new software for employees’ use but forgets to check if the staff can use the program. It does not matter if contractors, employees, outsourcing providers, or crowdsourcing will annotate the data. What is essential is that your workforce needs to access the tool and should have training on using the labeling tool. Determine if your annotating team has pre-existing knowledge of the commercial tool you will purchase or they have prior experience using the tool you will get. If they are beginners, you should be ready to have documentation about the annotation tool and the training to ensure that the team will be up to speed in no time. Likewise, you should have quality control procedures in place.
Using a partner or vendor
Consider that AI and machine learning is an iterative process, and there will be several changes as you work. Think about your needs as a service provider, the needs of your annotation team, and the project’s requirements. You can elect to work with a vendor that can be your collaborator as the project moves along and who is willing to develop the program to fit the current requirements.
Some providers choose to find a vendor that offers the right tool for a particular project. However, with the changes that occur as the annotation work progresses, you may need a flexible tool to respond to the changes.
When choosing a data annotation tool, ensure that you follow some criteria, including the tool’s efficiency, functionality, formatting, application (offline or online), and, lastly, the price. Even with the number of data annotation tools available, there is still a chance of making a mistake in the selection. Thus, evaluate how the tool will affect your other business processes, including data security, quality control, dataset administration, annotation methodologies, and HR management.