AWS Machine Learning Blog

Serving PyTorch models in production with the Amazon SageMaker native TorchServe integration

In April 2020, AWS and Facebook announced the launch of TorchServe to allow researches and machine learning (ML) developers from the PyTorch community to bring their models to production more quickly and without needing to write custom code. TorchServe is an open-source project that answers the industry question of how to go from a notebook […]

Activity detection on a live video stream with Amazon SageMaker

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 […]

Automating the analysis of multi-speaker audio files using Amazon Transcribe and Amazon Athena

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, […]

Learn from the winner of the AWS DeepComposer Chartbusters Spin the Model Challenge

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 now available in EU (Frankfurt) Region

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 […]

Reducing training time with Apache MXNet and Horovod on Amazon SageMaker

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, […]

Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio notebooks

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 […]

How Kabbage improved the PPP lending experience with Amazon Textract

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 […]

Ensure efficient compute resources on Amazon SageMaker

October 2022: 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 […]

Automated monitoring of your machine learning models with Amazon SageMaker Model Monitor and sending predictions to human review workflows using Amazon A2I

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 […]