AWS Machine Learning Blog

Amazon Elastic Inference (EI) now supports the latest version of TensorFlow­–1.12. It provides EIPredictor, a new easy-to-use Python API function for deploying TensorFlow models using EI accelerators. You can now use this new Python API function within your inference scripts as an alternative to using TensorFlow Serving when running TensorFlow models with EI. EIPredictor allows […]

 At re:Invent 2018, we announced Amazon SageMaker Neo, a new machine learning feature that you can use to train a machine learning model once and then run it anywhere in the cloud and at the edge. Today, we are releasing the code as the open source Neo-AI project under the Apache Software License. This release […]

At AWS, I regularly speak with AWS customers and AWS Partner Network (APN) partners about how they are using technology to transform human health. These companies often generate large amounts of health data that they use in a variety of applications, such as population health management and electronic health records. Developers need to find ways to use […]

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Amazon Comprehend Medical is a new HIPAA-eligible service that uses machine learning (ML) to extract medical information with high accuracy. This reduces the cost, time, and effort of processing large amounts of unstructured medical text. You can extract entities and […]

In this blog post, we’ll show you how to deploy an inference pipeline consisting of pre-processing using SparkML, inferences using XGBoost, and post-processing using SparkML. For this particular example, we are using the Car Evaluation Data Set from UCI’s Machine Learning Repository and training an XGBoost model to predict the condition of a car (i.e. unacceptable, acceptable, good, or very good).

More than ten years ago, simpleshow started to help their customers explain materials, ideas, and products by using three-minute animated explainer videos. These explainer videos use two hands and simple, black and white illustration to lead viewers through a story. Today, the company also provides mysimpleshow.com, a platform that allows anyone to produce high-quality explainer […]

Amazon SageMaker is a complete machine learning (ML) workflow service for developing, training, and deploying models, lowering the cost of building solutions, and increasing the productivity of data science teams. Amazon SageMaker comes with many predefined algorithms. You can also create your own algorithms by supplying Docker images, a training image to train your model […]

In the previous blog post, we showed you how to string together Amazon Transcribe and Amazon Comprehend to be able to conduct sentiment analysis on call conversations from contact centers. Here, we demonstrate how to leverage AWS CloudFormation to automate the process and deploy your solution at scale. Solution Architecture The following diagram illustrates architecture that […]

We’re excited to announce that Amazon Transcribe now supports automatic speech recognition in three new languages: French, Italian, and Brazilian Portuguese. These new languages expand upon the 5 languages already available in Amazon Transcribe: US English, US Spanish, Australian English, British English, and Canadian French. Using the Amazon Transcribe API, you can analyze audio files […]

Amazon SageMaker now comes pre-configured with the Scikit-Learn machine learning library in a Docker container. Scikit-Learn is popular choice for data scientists and developers because it provides efficient tools for data analysis and high quality implementations of popular machine learning algorithms through a consistent Python interface and well documented APIs. Scikit-Learn executes quickly and can […]