For over a decade now, tech companies have been investing heavily in machine learning (ML). Out of the two different types of ML, supervised learning has the most promising applications – but it is also more challenging as it requires you to properly sort raw data into tagged samples that offer training data to your budding AI.
This is the reason why finding the right solution is a top priority for any artificial intelligence projects.
Why is Data Annotation Important?
Automation is not only a buzzword in many industries today. AI opens up many opportunities for innovation, and a key component of this technology is data labelling. But, there is more that goes into the process than what most people realize.
Data labelling allows machines to develop an accurate understanding data and, with enough data, enable artificial intelligence to find meaning in raw, unlabeled data – thus giving AI a diverse list of applications across many industries. In 2019, the global market for data annotation boasted a value of $700 million, and the forecast for this sector is more promising still. And, for companies that choose a superior partner for data annotation, it also means a scalable edge against competitors.
What to look for when choosing software for AI data annotation
Choosing the right engine is critical to the viability and success of an AI model. Here are specific things you need to pay attention to when looking for a software, tool, or solution.
- You need to begin by determining what suits your requirements. There are different tools for annotating different types of data such as audio, text, image, and video. Consequently, you’ll also find that the corresponding storage capability, security, and type of supported file also varies according to the type of data.
- Identifying labelling accuracy goals is another guiding factor in selection. The higher the quality of results, the better performance you’ll get from the AI model. By implementing best practices in quality control, you can reduce common issues with managing your team.
- Apart from the role that your team plays in the process, technology is also vital. For instance, some tools pre-process data while also partially labelling other sets. Pre-processing will give you more confidence in the results, and also reduce your team’s workload.
- Know the important features of labelling tools. If your requirements are complex, you need a tool that will match these needs. As such, it will take time for the provider to customize or build a tool that will meet your goals. If possible, simplify your requirements so that you can choose a labelling tool that won’t require extensive customization.
Indeed, there are many different types of data labelling software and tools. Apart from paying attention to what your organization needs, it’s also vital to compare the features of these tools. Some software is suited to collaborative approaches. For example, if your company’s data labelling relies on crowdsourcing, you may want to look for software that supports sharing and collaboration.
In conclusion, the process of choosing a data labelling tool for AI models is no simple task. But by drilling down on the specifics of the project, you can find the right solution. Focusing on the project’s requirements and goals is perhaps the most essential step. It will ensure that you find and implement the most suitable solution resulting in a successful machine learning model.
Author | Emily Forbes
An Entrepreneur, Mother & A passionate tech writer in the technology industry!