One of the main reasons for this is the lack of reliable scaling of data annotation volumes and quality, which is often fueled by human error, unclear assumptions, ambiguity of images, and the subjective nature of the annotation task.
In the field of Machine Learning, having access to high-quality data is essential for developing high-performing models that can deliver business value in production. However, a recent survey of professionals working on ML projects showed that 78% of their projects stall at some stage before deployment. One of the main reasons for this is the lack of reliable scaling of data annotation volumes and quality, which is often fueled by human error, unclear assumptions, ambiguity of images, and the subjective nature of the annotation task.
In this webinar on How to Optimize Data Quality for Better ML Model Performance, Saul Miller, Vice President of Product Management, and his colleagues at Sama, share insights and best practices they have learned along the way in almost 15 years of annotation experience.
"Without ground truth data, there's no ML," says Saul Miller. Ground truth data is the high-quality data that is used to train ML models. It represents the actual truth about the data that we want the model to learn. The accuracy and completeness of ground truth data determine the quality of the ML model's output. The key to getting high-quality ground truth data is to ensure that the data annotation process is reliable, scalable, and of high quality.
Poor data quality can lead to incorrect model predictions, wasted resources, and lost business opportunities. Eric Zimmermann, an Applied Scientist in Machine Learning at Sama, notes that "annotation quality is the foundation of any machine learning project." Poor annotation quality can lead to a lack of confidence in the data, which in turn can result in lower model accuracy and less trust in the results. To mitigate these risks, it is essential to ensure that data annotation is of high quality, reliable, and scalable.
To ensure high-quality data annotation, it is essential to have clear guidelines, well-defined annotation tasks, and rigorous quality control processes. Pat Steeves, a Product Manager at Sama, notes that Sama uses a combination of automation and human quality control to ensure that data annotation is of the highest quality.
Automation can help to reduce the risk of human error, while human quality control can catch errors that automation may miss. To ensure that data annotation is reliable and scalable, it is also important to use well-defined processes and tools that can help to streamline the annotation process.
In conclusion, developing high-performing ML models that deliver business value in production requires high-quality data. The key to achieving high-quality data is to ensure that the data annotation process is reliable, scalable, and of high quality. By following best practices such as having clear guidelines, well-defined annotation tasks, and rigorous quality control processes, ML practitioners can mitigate the risks of poor data quality and improve their model's performance.
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