Human intelligence will always be necessary for data annotation and artificial intelligence. The margin of error for this job is quite small and gets smaller with time. This is because algorithms intended for public use frequently amplify minor mistakes. This increases the visibility of errors.

Artificial intelligence must be taught on inclusive and varied datasets to realize that utopian goal. That is accurate even if your next brilliant AI has a very narrow and singular goal in mind. You would be well to outsource to a seasoned data annotation service provider to realize your next great breakthrough in automation, machine learning, or artificial intelligence.

What are the biggest challenges for large-scale data annotation projects?

• At-Scale Accuracy and Quality

Internal AI project teams eventually face a quantity vs. quality problem as the need for AI dataset volumes increases for training or instructing the model to make future decisions. Teams working on AI projects must set up and evaluate quality control procedures to ensure that annotation at scale doesn’t compromise quality.

• Speed

The majority of AI initiatives have deadlines. Annotating datasets with millions of data points can quickly turn into a bottleneck that causes delays or gives a rival company more time to launch a solution. Time and resources for project-specific training as well as sufficient time set out for annotation itself are essential components of a successful data annotation strategy.

• Human Resources

Hiring inside personnel for data annotation is a possibility when projects get bigger. However, it takes months for new hires to become capable of meeting quality requirements and working autonomously.

It could be alluring to put other members of the AI project team in charge of data annotation in an all-hands-on manner. But data labeling requires particular abilities, such as consistency, outstanding short-term memory, patience, and attention to detail. 

• Agility

Many businesses lack the manpower to finish large-scale data annotation projects internally within the timeframes that they would like.

Teams working on agile AI projects need to budget for tasks after the initial round of annotations. Building AI models is an iterative process that requires updating or changing datasets to improve the model’s output. Increasing agility in response to changing requirements can be achieved by automating certain human-supporting tasks, including results validation.

• Various Challenges for Multiple Data Annotation Types 

There are many different kinds of data and also many different kinds of data annotation. Each comes with various challenges. Additionally, various use cases and project goals provide their unique problems. Unique problems that may require customized solutions. For example, the image and video annotation required for computer vision and machine learning comes in different forms to provide solutions for different applications.

Types of data annotation and labeling services include:

• Bounding Boxes for Object Detection
• Polygons for Semantic & Instance Segmentation
• Points for facial recognition & body pose detection
• Texts for image captioning
• Select for image classification
• Semantic Segmentation for complex image classification

How to overcome the challenges of Data Annotation? There is a solution for every problem

You can overcome the ethical and technical obstacles in the way of developing your revolutionary AI. Science fiction of the past is the science reality of the future. There is no need for your business to handle it alone. One excellent way to handle your data annotation project is to outsource it. While, your team and resources can be freed up to focus on your key objectives.

A top-notch platform-equipped data labeler or annotation service provider can create personalized remedies for your particular issues. You don’t need to experience knowledge overload. It’s okay to delegate collecting, safely storing, and processing of that massive amount of data to others.

Here’s where Data Labeler comes into the picture!

While you concentrate on developing algorithms that will benefit you, let Data Labeler concentrate on your data labeling & annotation techniques. 

Still, wondering how Data Labeler can help you? Or have a Use Case in mind? Let’s Discuss. Contact us