The discipline of data annotation and labeling is growing in popularity and significance throughout the world. The global market for data annotation tools is expected to reach $2.57 billion by 2027, according to a published report.
For robots, drones, and vehicles to gain increasing levels of autonomy, artificial intelligence-based on correct data is essential. Companies must create a balance between research, development, analysis, and other activities linked to their core duties in order to implement machine learning efforts.
It’s possible that one’s own set of manpower is not sufficient nor have enough time to annotate huge volumes of data in order to train machine learning algorithms. Engineers and other team members may be paid well, making this a prohibitively expensive employment.
Because data annotation takes a long time, many businesses outsource it to firms with the appropriate staffing capacity to complete the project on time and on budget. Using expert data annotation services to save expenses and increase productivity is thus a “cost-effective option”. Text annotation services, image annotation services, video annotation services, and content moderation services are all examples of commonly outsourced data support for AI/Machine Learning services.
In today’s post, we’re going to look at some aspects of data annotation you should consider before diverting your staff from their everyday workloads to label hundreds or even thousands of training data.
Training data accuracy and quality are critical to the success of a machine learning solution. The quality of your annotated data can decide your project’s outcome, no matter how well-funded it might be. A huge advantage of outsourcing data annotation is that professional teams like TRU29 SOLUTIONS feature skilled, experienced professionals who work much faster and more accurately than most internally resourced teams.
They have access to instructional guidelines and purpose-built tools for data annotation — and they are accustomed to processing large volumes of data. This means they can ensure a high level of accuracy while maintaining the speed and productivity your project requires to complete on deadline. TRU29 SOLUTIONS trains and tests the manpower before they are even assigned a task, and has multiple quality checks and controls built into both the workforce management processes and data annotation platform. This ensures the highest level of data quality.
Scale & Flexibility
Machine Learning/Annotation projects typically require thousands OR even millions of labeled training items to be successful. While the goals of machine learning projects can vary widely in complexity, they all share a common requirement – “a large volume of high-quality data”. Most companies simply don’t have the existing resources to staff for large-scale data annotation projects, and it’s expensive to pull engineers and other team members off of their core work.
To cover the spread of data your system might encounter in the real world, outsourcing can provide a large, on-demand staff of qualified workers to perform these tasks. And because unique requirements can emerge as a data annotation project progresses, the ability to adapt and scale up without losing data quality is critical. Internally resourced annotation teams may not have the required experience or bandwidth to handle large amounts of data or shift project needs. TRU29 SOLUTIONS’ team is accustomed to annotating huge volumes of data, and rapidly responding to requests for more or different types of data and metadata.
With TRU29 SOLUTIONS’ global resources, we can also help extend your product globally, localizing it for new markets using data from in-market annotators — native speakers with a grasp of local cultural nuance. This is an important aspect of projects involving language-based products, for example.
Relying on an internal team for annotation might delay the completion of your project, as these employees already have full-time obligations to attend to in addition to annotating hundreds of images. There will also be some training and ramping-up with these employees, and that can take time. If your project lacks urgency, slower time-to-completion might be acceptable, but many companies with Machine Learning projects feel pressure to get a product to market before competitors beat them to the punch. Outsourcing your annotation project to a highly trained, dedicated team can mean the difference between weeks and months.
Another benefit of outsourcing is that the service can rapidly recruit data annotators with specific requirements — such as native speakers for a target demographic — and can easily ramp up and ramp down the crowd of annotation workers as project needs fluctuate. By outsourcing to a vendor that takes a managed services approach like TRU29 SOLUTIONS, everything from consulting to annotation task design to workforce management and quality assurance is handled externally, with repeatable processes.
Data security is the highest priority on many machine learning projects. Some companies don’t think that they can outsource data annotation due to data privacy concerns like GDPR, compliance (such as PII or PHI), or other sensitive data-related considerations. To that end, TRU29 SOLUTIONS offers annotators working in one of our secured operation sites, on-site workers using an air-gapped, on-prem deployment of our platform, or on-site workers working within our customers’ proprietary tools. TRU29 SOLUTIONS’ secure facilities are supported by a business continuity plan to handle any possible circumstances.
Using internal resources to annotate your data is tempting and might be great for small, simple Machine Learning projects. To help ensure success, though, outsourcing projects to a company with years of experience and highly skilled personnel is the right choice for many organizations.
At TRU29 SOLUTIONS, we’ve assisted various companies globally in machine learning and AI scale their projects from proof of concept to production. Contact us now and let our team assist you to leverage outsourcing as part of your strategy.