AWS Machine Learning Blog

Tag: Amazon Sagemaker

AWS internal use-case: Evaluating and adopting Amazon SageMaker within AWS Marketing

We’re the AWS Marketing Data Science team. We use advanced analytical and machine learning (ML) techniques so we can share insights into business problems across the AWS customer lifecycle, such as ML-driven scoring of sales leads, ML-based targeting segments, and econometric models for downstream impact measurement. Within Amazon, each team operates independently and owns the […]

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Amazon SageMaker console now supports training job cloning

Today we are launching the training job cloning feature on the Amazon SageMaker console, which makes it much easier for you to create training jobs based on existing ones. When you use Amazon SageMaker, it’s common to run multiple training jobs using different training sets and identical configuration. It’s also common to adjust a specific […]

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Using R with Amazon SageMaker

This blog post describes how to train, deploy, and retrieve predictions from a machine learning (ML) model using Amazon SageMaker and R. The model predicts abalone age as measured by the number of rings in the shell. The reticulate package will be used as an R interface to Amazon SageMaker Python SDK to make API calls to Amazon […]

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Using Pipe input mode for Amazon SageMaker algorithms

Today, we are introducing Pipe input mode support for the Amazon SageMaker built-in algorithms. With Pipe input mode, your dataset is streamed directly to your training instances instead of being downloaded first. This means that your training jobs start sooner, finish quicker, and need less disk space. Amazon SageMaker algorithms have been engineered to be […]

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Perform a large-scale principal component analysis faster using Amazon SageMaker

In this blog post, we conduct a performance comparison for PCA using Amazon SageMaker, Spark ML, and Scikit-Learn on high-dimensional datasets. SageMaker consistently showed faster computational performance. Refer Figures (1) and (2) at the bottom to see the speed improvements. Principal Component Analysis Principal Component Analysis (PCA) is an unsupervised learning algorithm that attempts to […]

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Running fast.ai notebooks with Amazon SageMaker

fast.ai is an organization dedicated to making the power of deep learning accessible to all. They have developed a popular open source deep learning framework called fast.ai. This technology is based on the deep learning library PyTorch, which is focused on usability and allows users to create state-of-the-art models with just a few lines of code in domains […]

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Build a March Madness predictor application supported by Amazon SageMaker

What an opening round of March Madness basketball tournament games! We had a buzzer beater, some historic upsets, and exciting games throughout. The model built in our first blog post (Part 1) pointed out a few likely upset candidates (Loyola IL, Butler), but did not see some coming (Marshall, UMBC). I’m sure there will be […]

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Create a Word-Pronunciation sequence-to-sequence model using Amazon SageMaker

Amazon SageMaker seq2seq offers you a very simple way to make use of the state-of-the-art encoder-decoder architecture (including the attention mechanism) for your sequence to sequence tasks. You just need to prepare your sequence data in recordio-protobuf format and your vocabulary mapping files in JSON format. Then you need to upload them to Amazon Simple […]

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Customize your Amazon SageMaker notebook instances with lifecycle configurations and the option to disable internet access

Amazon SageMaker provides fully managed instances running Jupyter Notebooks for data exploration and preprocessing. Customers really appreciate how easy it is to launch a pre-configured notebook instance with just one click. Today, we are making them more customizable by providing two new options: lifecycle configuration that helps automate the process of customizing your notebook instance, […]

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