Data Annotation Benefits and Advantages in 2023

When building an artificial intelligence (AI) or machine learning (ML) model, model performance is one of the most important metrics you need to care about. Lack of trusted data leads to lost money, time, and business value. Analysts agree that between 70-80% of AI or ML models fail because of poor quality training data, inability to deliver quality results at scale, and a combination of human and procedural errors associated with the large volumes of training data that is required.

What is Data Annotation?

Data annotation is an essential part of ML workflows that involve image and video classification. For AI and ML models to work properly, they must be trained to understand specific information. In simple terms, data annotation is the process of labeling and categorizing data to create a ground truth from which a ML algorithm learns. With high-quality, human-in-the-loop (HITL) data curation, annotation, and validation, enterprises can dramatically reduce the risk of the models failing as they scale.

Importance of Good Quality Data Annotation

The quality of data annotation directly affects the accuracy and reliability of ML algorithms. Good quality data annotation ensures that the algorithm can accurately identify and classify images and videos, which is especially important in applications such as object detection, facial recognition, advanced driver assistance systems and autonomous vehicles.Inaccurate or incomplete data annotation can lead to biased ML models, which can have significant consequences in real-world applications. Therefore, it is crucial to ensure that data annotation is done accurately and consistently.

De-risking ML Models

ML models often fail when they scale. The algorithms get muddy because of human error, unclear instructions, ambiguity of images and sounds, and the subjective nature of the annotation task. These risks can stem from any of the following:

• Incomplete data documentation;
• Poor annotation instructions;
• Unskilled annotators;
• Lack of automation and repeatability;
• Insufficient budget to support the amount of data your model requires; and
• Poor scoping of annotation projects any of which can lead to the ML model failing to produce results.

More specifically, model errors can result from the following:

• Inaccurate or inconsistent labels leading to ML models that underperform, which can have significant consequences in real-world applications.
• Labeling bias that occurs when annotators unintentionally or intentionally label data in a way that is biased or skewed, leading to ML models that make erroneous predictions.
• Failure to provide quality data at scale is a result of time-consuming and resource-intensive processes.

Cost Benefits and Advantages of Good Quality Data Annotation

Good quality data annotation can deliver significant cost benefits and advantages for a ML project.

1. Increased data annotation accuracy leads to more reliable ML models, thereby improving the efficiency and effectiveness of the project.
2. Accurate and consistent data annotation reduces the time it takes to develop and deploy ML models, which can give businesses a competitive advantage.
3. Good quality data annotation can reduce the need for rework and corrections, which can save time and money in the long run.

Your ML Model’s Success Requires More Than Data

Sama provides a data centric ecosystem for computer vision. Good quality data curation, annotation, and validation are essential for successful ML workflows involving image and video classification.Sama delivers best-in-class data annotation solutions with our enterprise-strength, experience & expertise, and ethical AI approach. We go beyond your data to help you deliver the business outcome you require from your ML model. This unique combination enables us to always deliver the data quality and actionable insights needed for today’s leading enterprise companies - covering both the common use cases and the most complex edge cases. This is why enterprises come to Sama when other data providers fail. They rely on us to get their AI investments into production faster, keep them there, and deliver real ROI.