Category: Amazon SageMaker

August 2021: Post updated with changes required for SageMaker SDK v2, courtesy of Eitan Sela, Senior Startup Solutions Architect In the United States, with the 2018 midterm elections approaching, people are looking for more information about the voting process. This blog post explores how we can apply machine learning (ML) to better integrate science into […]

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 […]

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 […]

Once you have trained, optimized and deployed your machine learning (ML) model, the next challenge is to host it in such a way that consumers can easily invoke and get predictions from it. Many customers have consumers who are either external or internal to their organizations and want to use the model for predictions (ML […]

July, 2022: This post was reviewed and updated for relevancy and accuracy, with an updated AWS CloudFormation Template. December 2020: Post updated with changes required for Amazon SageMaker SDK v2 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 […]

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 […]

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 […]

Update 25 JAN 2019: fast.ai has released a new version of their library and MOOC making the following blog post outdated. For the latest instructions on setting up the library and course on a SageMaker Notebook instance please refer to the instructions outlined here: https://course.fast.ai/start_sagemaker.html fast.ai is an organization dedicated to making the power of deep learning accessible […]

Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. But besides streamlining the machine learning (ML) workflow, Amazon SageMaker also provides a serverless, powerful, and easy-to-use compute environment to execute and parallelize a large spectrum of scientific computing […]

In this blog post, I want to continue the theme of demonstrating agility, cost efficiency, and how AWS can help you innovate through your customer analytics practice. Many of you are exploring how AI can enhance their customer 360o initiatives. I’ll demonstrate how targeted campaigns can be driven by machine learning (ML) through solutions that leverage Amazon SageMaker and Amazon Pinpoint.