Machine Learning with Julia on AWS SageMaker

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to quickly build, train, and deploy machine learning (ML) models without worrying about the infrastructure. Julia is a high-level, high-performance, dynamic programming language. While it is a general-purpose language and can be used to design various applications, many of its features are well suited for numerical analysis and computational science. By running Julia on SageMaker, you will be able to get the most out of this programming language as you can easily access high-performance instances.

An Amazon SageMaker notebook instance is a machine learning (ML) compute instance running the Jupyter Notebook App. SageMaker manages provisioning-related resources. You can use Jupyter notebooks in your notebook instance to prepare and process data, write code to train/validate models, and deploy them as SageMaker endpoints. You can create multiple notebooks within your notebook instance.

To create a notebook instance, you should go to Notebook instance in the AWS SageMaker console, and click on the Create Notebook instance button:

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