Category: Artificial Intelligence

Live video streams are continuously generated across industries including media and entertainment, retail, and many more. Live events like sports, music, news, and other special events are broadcast for viewers on TV and other online streaming platforms. AWS customers increasingly rely on machine learning (ML) to generate actionable insights in real time and deliver an […]

In an effort to drive customer service improvements, many companies record the phone conversations between their customers and call center representatives. These call recordings are typically stored as audio files and processed to uncover insights such as customer sentiment, product or service issues, and agent effectiveness. To provide an accurate analysis of these audio files, […]

Support for AWS DeepComposer will be ending soon. Please see Support for AWS DeepComposer ending soon for more details. 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 […]

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, […]

April 2025: This post was reviewed and updated for accuracy. The Amazon SageMaker Studio Image Build convenience package allows data scientists and developers to easily build custom container images from your Studio JupyterLab notebooks via CLI. The CLI eliminates the need to manually set up and connect to Docker build environments for building container images […]

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 […]