AWS Machine Learning Blog

Amazon SageMaker now allows customers to update or disassociate lifecycle configurations for notebook instances with the renewed APIs. You can associate, switch between, or disable lifecycle configurations as necessary by stopping your notebook instance and using the UpdateNotebookInstance API at any point of the notebook instance’s lifespan. Lifecycle configurations are handy when you want to organize and automate the setup that is […]

Amazon SageMaker built-in algorithms now support Pipe mode for fetching datasets in CSV format from Amazon Simple Storage Service (S3) into Amazon SageMaker while training machine learning (ML) models. With Pipe input mode, the data is streamed directly to the algorithm container while model training is in progress. This is unlike File mode, which downloads […]

AWS recently released Model Server for Apache MXNet (MMS) v1.0, featuring a new API for managing the state of the service, which includes the ability to dynamically load models during runtime, to lower latency, and to have higher throughput. In this post, we will explore the new features and showcase the performance gains of the […]

Natural disasters like the 2017 Santa Rosa fires and Hurricane Harvey cost hundreds of billions of dollars in property damages every year, wreaking economic havoc in the lives of homeowners. Insurance companies do their best to evaluate affected homes, but it could take weeks before assessments are available and salvaging and protecting the homes can […]

Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Today, we are launching 113 new language pairs. Customers can now translate between currently supported languages, such as French to Spanish for example, with a single API request. With this update, we are expanding the number of supported language pairs […]

This post was reviewed June, 2022. An Amazon SageMaker notebook instance provides a Jupyter notebook app through a fully managed machine learning (ML) Amazon EC2 instance. Amazon SageMaker Jupyter notebooks are used to perform advanced data exploration, create training jobs, deploy models to Amazon SageMaker hosting, and test or validate your models. The notebook instance […]

Amazon SageMaker now supports running Batch Transform jobs in Amazon Virtual Private Cloud (Amazon VPC) and using AWS Key Management Service (AWS KMS). Amazon VPC allows you to control access to your machine learning (ML) model containers and data so that they are private and aren’t accessible over the internet. AWS KMS enables you to encrypt […]

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. August 30, 2023: Amazon Kinesis Data Analytics has been […]

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. The service does the following for you: Identifies the language of the text. Extracts key phrases, places, people, brands, or locations. Understands how positive or negative the text is. Analyzes text using tokenization and parts […]

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. AWS DeepLens is a deep-learning-enabled video camera for developers. It […]