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Amazon SageMaker comes with two options to spin up fully managed notebooks for exploring data and building machine learning (ML) models. The first option is fast start, collaborative notebooks accessible within Amazon SageMaker Studio - a fully integrated development environment (IDE) for machine learning. You can quickly launch notebooks in Studio, easily dial up or down the underlying compute resources without interrupting your work, and even share your notebook as a link in few simple clicks. In addition to creating notebooks, you can perform all the ML development steps to build, train, debug, track, deploy, and monitor your models in a single pane of glass in Studio. The second option is Amazon SageMaker Notebook Instance - a single, fully managed ML compute instance running notebooks in cloud, offering customers more control on their notebook configurations. Today, we are excited to announce that both SageMaker Studio and SageMaker Notebook Instance now come with JupyterLab 3 notebooks to boost productivity of data scientists and developers building ML models on SageMaker. View the full article -
Amazon SageMaker JumpStart helps you quickly and easily solve your machine learning problems with one-click access to (a) more than 300 popular model collections from TensorFlow Hub, PyTorch Hub, Hugging Face and Gluon CV, and (b) 18 end-to-end solutions that solve common business problems such as demand forecasting, fraud detection and document understanding. The available models can be used for a wide range of machine learning tasks including image classification, object detection, semantic segmentation, instance segmentation, image embedding, text classification, sentence pair classification, question answering, text embedding, text summarization, text generation, machine translation, tabular classification and tabular regression. View the full article
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