MLOps and Regression Testing

Introduction:

2D and 3D annotation tools are commonly used in a variety of applications, including computer vision, video analysis, and robotics. These tools allow human annotators or machine learning frameworks to label and classify objects or events in images or videos, providing important information for training and improving machine learning models. However, manual annotation can be time-consuming and error-prone, leading to the development of techniques for pre-populating annotation tools with the output of machine learning models. In this white paper, we will explore how machine learning models can be used to pre-populate 2D and 3D annotation tools, and how the resulting supercharged dataset can be incorporated into an MLOps pipeline to improve the performance of machine learning models.

Pre-populating Annotation Tools with ML Output:

One way to pre-populate annotation tools with machine learning output is to use machine learning models to perform initial labeling and classification of objects or events in images or videos. These models can be trained on large datasets of labeled data, and can be used to label new data with a high degree of accuracy. The resulting labels can then be rendered and showcased to human annotators or machine learning frameworks, providing a starting point for further annotation and refinement.

Using the output of machine learning models to pre-populate annotation tools can significantly reduce the time and effort required for manual annotation. It can also improve the accuracy of the final dataset, as the machine learning models can identify patterns and features that may be difficult for human annotators to discern. This can be particularly useful in cases where the objects or events being labeled are small or subtle, or where the data contains a large number of samples.

Improving ML Models with Supercharged Datasets:

Once the annotation process is complete, the resulting supercharged dataset can be used to retrain and improve machine learning models. The additional labels and annotations provided by the pre-populated annotation tools can provide valuable additional information, allowing the models to learn more about the objects or events being classified. This can result in higher accuracy and improved performance on real-world tasks.

The supercharged dataset can be incorporated into an MLOps pipeline, allowing the updated machine learning models to be deployed and tested in a production environment. This process can be repeated iteratively, with the output of machine learning models being used to pre-populate annotation tools and the resulting supercharged dataset being used to improve the performance of the models.

Regression Testing:

While the use of pre-populated annotation tools and supercharged datasets can significantly improve the performance of machine learning models, it is important to carefully test and validate the updated models to ensure that they do not degrade in other scenarios. This can be achieved through the use of regression testing, which involves comparing the performance of the updated model with a baseline model on a variety of tasks and datasets.

Regression testing can help mitigate the “gopher effect,” in which improving the performance of a machine learning model in one scenario leads to degraded performance in another. By thoroughly testing the updated model, it is possible to identify any areas where it may be performing worse than the baseline model, and to adjust the model or the training data accordingly.

Conclusion:

Pre-populating annotation tools with the output of machine learning models can significantly reduce the time and effort required for manual annotation, while also improving the accuracy and performance of machine learning models. By incorporating the resulting supercharged dataset into an MLOps pipeline, it is possible to retrain and improve machine learning models iteratively, while also using regression testing to ensure that the updated models do not degrade in other scenarios.