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Computer Vision on AWS
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Once you have a training dataset, you move to the model development stage, which includes training, evaluating, and tuning a model. Algorithm selection is not a one-size-fits-all process and model development is iterative. In this section, we will not dive deep into training algorithms, but will instead highlight a few best practices and the training options available with AWS AI/ML services.
In Chapter 1, we briefly covered a few types of algorithms that are often used to solve CV use cases, such as object detection, classification, and segmentation. There are many algorithms available to solve CV problems and choosing the best one depends on a few different factors, including the type of problem you are trying to solve, the data you have available, and your specific performance requirements. Deep learning algorithms often work well for CV, but other more traditional ML algorithms such as Support Vector Machines (SVMs), random forests, and k-nearest...