Data Labeling Workloads: How Outsourcing Can Help

Annotating training data imagery can be a daunting challenge. The standard frame rate for a commercial video is 24 frames per second, that means that in order to correctly label a 5 minute long piece of footage an annotator needs to process 7200 individual images.

At a conservative estimate of 30 seconds per image this task represents 60 hours of labour for an individual. This kind of workload pressure is applicable to a wide range of computer vision artificial intelligence (AI) projects.

In order to create the best machine learning models developers need access to high quality training data at the appropriate scales. Developing the capacity to carry out this work in-house presents a number of challenges for technology companies. Outsourcing to dedicated data annotation services can help solve a number of these problems.

In-House Data Annotation: Overcoming the Challenges

There are four core issues that can negatively impact in-house data labeling operations. Each of these difficulties can be alleviated by collaborating with professional annotation services:

• Cost: Data labeling in-house can be prohibitively expensive. Developing the capacity to feed data hungry algorithms can be a significant financial burden, particularly for startups. The cost of hiring staff, providing office space and purchasing annotation tools can be significant.

Solution: External data annotation services are able to offer competitive pricing for a range of needs. Providers, like Keymakr, can help companies save money without sacrificing quality.

• Training: Creating an in-house annotation operation means lots of training. Inexperienced annotators need to be instructed in how to operate annotation tools and advised on the specific needs of a project. Creating and managing a training program entails a great deal of time and attention. This has the potential to divert resources and expertise that could be more profitably used for research & development.

Solution: Image annotation specialists employ trained, professional annotators who are able to quickly adapt to any demand and who are familiar with a range of annotation tools.

• Management: Management can be a particular challenge when data annotation is taking place within a startup or larger organisation. Individuals in leadership positions and senior members of staff may find themselves devoting a significant amount of time to the administration and management of large data annotation teams.
  
  The burden of troubleshooting and safeguarding quality in training datasets also falls upon these key individuals. In-house data annotation can distract innovators and experts from their core mission.

Solution: By transferring management responsibilities to professional annotation managers companies are able to free up leaders and data scientists to do what they do best. Troubleshooting can be communicated to experienced service providers, such as Keymakr, who are able to respond quickly to rectify any problem.

• Scalability: More often than not in-house annotation teams are small, and designed to fill one specific need. Of course data needs fluctuate over time, more images might be needed one week than the next. As a consequence in-house teams can be overloaded with work at times and under worked at others. As companies grow and data needs change these kinds of inefficiencies can begin to affect the bottom line.

Solution: Outsourcing to competent, experienced annotation professionals can enable companies to scale up and scale down data annotation in line with what their machine learning models need, without the costly inefficiencies.

Outsourcing Image Annotation to Facilitate Your Project

In-house teams allow companies to exercise control over data annotation but as has been shown this can create significant disadvantages. Outsourcing to experienced annotation providers can ease many of these burdens.

Keymakr provides a professional, managed annotation service that meets your demands for accuracy, flexibility and affordability.