The Role of Image Annotation in Machine Learning Engineering
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Loading... - Pascale Messi
- 19 Aug 2022
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Machine learning engineers (ML engineers) are IT professionals who design, build, and operate artificial intelligence (AI) systems. ML engineers typically work as part of a large team, together with data scientists, data analysts, data engineers, and data architects. ML engineers serve as a bridge between data scientists, who focus on statistics and model building, and the operational AI systems that make it possible to train models and deliver them to end users effectively.
The role of a machine learning engineer is to evaluate, analyze, and organize large amounts of data while running tests and optimizing machine learning models and algorithms. ML engineers are also responsible for ensuring that AI systems can run effectively in production and meet the required service level agreements (SLAs).
What is Image Annotation?
Annotating an image is a manual operation that assigns a meaningful, textual label to the image as a whole and individual objects within it. An important part of image annotations is delineating specific areas within the image, which may be rectangles (bounding boxes) or complex polygonal shapes. The set of labels is typically pre-determined by data scientists and used to inform computer vision models about the information displayed in the image.
When annotating images, each image can be assigned one or more labels. For simple image classification algorithms, one label per image might be sufficient, but object detection algorithms require annotations of objects within the image. If there are multiple labels, the annotator must indicate the area of the image that corresponds to each label using a bounding box or pixel map.
Why is Image Annotation so Important in Machine Learning?
Image annotations are a foundation of computer vision algorithms because they create the training data that is the input for supervised learning algorithms. High-quality annotations will allow a computer vision model to see the world and derive accurate insights. Low-quality annotations result in models that do not have a good sense of the relevant real-world objects and thus perform poorly.
Annotated data is specifically needed if a model solves a relatively new domain. Standard datasets exist for common tasks like image segmentation and classification. These pre-trained models can be adapted to specific uses with only a small amount of training data, using transfer learning techniques.
Training a new model from scratch typically requires a large amount of annotated image data, which must have sufficient images in the training, validation, and test sets. Creating such a dataset can be a major effort for any organization. Read More...
