AWS Machine Learning Blog

AWS is excited to announce the winner of the second AWS DeepComposer Chartbusters challenge, Lena Taupier. AWS DeepComposer gives developers a creative way to get started with machine learning (ML). In June, we launched the Chartbusters challenge, a global competition where developers use AWS DeepComposer to create original compositions and compete to showcase their ML […]

Amazon Personalize is a machine learning (ML) service that enables you to personalize your website, app, ads, emails, and more with private, custom ML models that you can create with no prior ML experience. We’re excited to announce the general availability of Amazon Personalize in the EU (Frankfurt) Region. You can use Amazon Personalize to […]

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. As datasets continue to increase in size, […]

The new Amazon SageMaker Studio Image Build convenience package allows data scientists and developers to easily build custom container images from your Studio notebooks via a new CLI. The new CLI eliminates the need to manually set up and connect to Docker build environments for building container images in Amazon SageMaker Studio. Amazon SageMaker Studio […]

This is a guest post by Anthony Sabelli, Head of Data Science at Kabbage, a data and technology company providing small business cash flow solutions. Kabbage is a data and technology company providing small business cash flow solutions. One way in which we serve our customers is by providing them access to flexible lines of […]

November 2023: This post was reviewed and updated for accuracy. The adaptability of Amazon SageMaker allows you to manage more tasks with fewer resources, resulting in a faster, more efficient workload. SageMaker is a fully managed service that allows you to build, train, deploy, and monitor machine learning (ML) models. Its modular design lets you […]

When machine learning (ML) is deployed in production, monitoring the model is important for maintaining the quality of predictions. Although the statistical properties of the training data are known in advance, real-life data can gradually deviate over time and impact the prediction results of your model, a phenomenon known as data drift. Detecting these conditions […]

This guest post was authored by Kazuma Ohara, Director of RENGA Inc., and edited by Yumiko Kanasugi, Solutions Architect at AWS Japan. RENGA Inc. operates Mansion Note, one of Japan’s most popular condominium review and rating websites, which gets over a million unique visitors per month. Mansion Note provides a service where people can check […]

This post is written in collaboration with DevFactory, an AWS Select Technology Partner. DevFactory is an enterprise SaaS-focused company that is responsible for innovation, development, and operation of over 120 enterprise products. DevFactory also offers DevGraph, an integrated suite of software development tools built on AWS. Amazon CodeGuru is an automated code review service that […]

TensorBoard is an open source toolkit for TensorFlow users that allows you to visualize a wide range of useful information about your model, from model graphs; to loss, accuracy, or custom metrics; to embedding projections, images, and histograms of weights and biases. This post demonstrates how to use TensorBoard with Amazon SageMaker training jobs, write […]