5 Best Practices To Speed Up Your AI Projects With Effective Data Annotation

By Umang Dayal
February 2, 2024

A California-based company used an AI model that was trained using video annotation through a combination of human annotators and automated tools to read motion, visuals, and label targets in the video footage. This allowed the company to use its AI model to predict traffic congestion, improve road planning, and prevent road accidents. 

Artificial intelligence and automation systems are getting more intelligent with better inputs used to develop these AI models. Various computer vision algorithms gather and train data sets to enhance robotics, drones, self-driving cars, etc. Training data can be a lengthy process if you don’t follow a definitive strategy and objective-based planning for an effective data annotation process. In this blog, we will discuss 5 best practices to speed up the data annotation process. 

What is Data Annotation in Machine Learning?

Data annotation is the process of creating data sets like text, images, and videos for computer vision algorithms. The data labeling process follows a specific technique to annotate data for text, images, and videos as an initial input that can be supplied to machine learning algorithms which read and understand it to perform accurate outputs. 

Why Data Annotation is Important?

Data labelling is the backbone of AI models which enables them to perform functions using the provided data sets and make predictions to create new functions. This process involves data labeling of relevant tags, metadata, and annotations, which helps the system to identify patterns and make accurate decisions. Data annotation is what determines the accuracy, performance, and accuracy of AI and machine learning models. 

There are various strategies involved in the data annotation process which include image annotation, video or audio annotation, text annotation, LiDAR annotation, and more. Each technique can be used for unique AI-specific projects. For example, automated cars use a highly trained data set that is used by large automotive companies such as Tesla, to build and operate in real-time situations. 

5 Ways to speed up your AI projects with effective data annotation methods?

Use Ground Truth Data Annotation 

Ground truth data annotation refers to human-verified data that can be used as facts. When you involve humans in the verification and classification of data sets the algorithm's logical decision-making accuracy goes high and you get accurate outputs. You need these accurately trained datasets while creating a foundation for your AI projects. Ground truth data labeling can fast-track your annotation process and maximize quality. 

Decide The Type of Annotation

Before starting the data annotation process you should decide the type of annotation your project requires. This will make complicated functions simpler in the long run i.e. streaming services or online shopping platforms. Let’s discuss a few use cases for more clarity. 

While using Image annotation keywords, tags, captions, identifiers, etc, to help the AI model read annotated data as a different item. These algorithms can then understand and classify these set parameters and learn automatically. A Swiss food waste solution company trained thousands of food images to train their AI model. This company has helped world-renowned restaurants and hotels tackle the problem of food wastage by instantly analyzing food waste using their AI model. 

Similarly, text annotation is used to classify emotions, fun, anger sarcasm, or abstract language. Moreover, text annotation and audio annotation are disrupting the music and entertainment industries as we speak. 

Many manual annotation tools offer a friendly user interface and intuitive functionality that can make your data labeling process easier. They offer a range of annotation tools such as bounding boxes, cuboids, polygons, key points, instance segmentation, semantic segmentation, and more.

Combine Artificial and Human Intelligence

A combination of humans and AI is the perfect blend to build the most efficient and effective AI models. AI systems have been developed that can make optimal decisions with large data sets but nothing can surpass the human recognition pattern with even small or poor quality data sets. Leveraging the human annotator's abilities and machine learning’s target mapping for large datasets can be the best approach to speed up AI projects with an effective data annotation strategy. 

Learn more: Why Data Annotation Still Needs a Human Touch

Adopt Latest Technologies 

In the global AI industry, we are seeing huge adoption of automated labeling for speeding up the annotation process and improving the security and accuracy of data sets. You can leverage these latest trends to gather large sets of data and reduce manual input for faster results. 

Neurosymbolic AI has increased the statistical knowledge of ML frameworks and reduced dependency on humans. In turn, you can save a lot of time, costs, and effort in the whole data annotation process.

For large data, you can significantly speed up your entire labeling process by leveraging AI tools that can label data points based on predefined patterns or rules from existing trained annotations. SuperAnnotate is one such example that uses ML to accelerate your data labeling process. It offers features like auto annotation of data sets and active learning that are perfect for large annotation projects.

Learn more: Human-Powered Data Annotation vs Tools/Software

Outsource Your Data Annotation Project

When acquiring correct data sets and performing the data labeling process gets complicated and costly you should consider levering the services of data annotation solution-based companies. These companies are experts at labeling and training machine learning algorithms with the correct data sets, this will allow you to speed up your development project by focusing on your expertise in artificial intelligence. These third-party data labeling companies offer highly accurate trained data sets that can be customized as per your project needs. 

Conclusion

If you are running on a tight deadline and want to speed up your AI project’s data annotation you should leverage ground truth, identify your data annotation requirement, use combined efforts of human and machine annotators, use the latest technologies, and consider outsourcing your data annotation process to a third party.

By speeding up and scaling your AI projects businesses can acquire a competitive advantage in this data-driven world. The accuracy and effectiveness of your AI models depend on meaningful annotations that can drive innovation and business value. You can explore DDD’s computer vision data annotation services to fully annotate your AI projects.