Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, image, and text.
This post is the second in a series on the new built-in algorithms in SageMaker. In the first post, we showed how SageMaker provides a built-in algorithm for image classification. Today, we announce that SageMaker provides a new built-in algorithm for object detection using TensorFlow. This supervised learning algorithm supports transfer learning for many pre-trained models available in TensorFlow. It takes an image as input and outputs the objects present in the image along with the bounding boxes. You can fine-tune these pre-trained models using transfer learning even when a large number

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