AWS Machine Learning Blog

Category: Artificial Intelligence

Now easily perform incremental learning on Amazon SageMaker

Data scientists and developers can now easily perform incremental learning on Amazon SageMaker. Incremental learning is a machine learning (ML) technique for extending the knowledge of an existing model by training it further on new data. Starting today both of the Amazon SageMaker built-in visual recognition algorithms – Image Classification and Object Detection – will […]

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Direct access to Amazon SageMaker notebooks from Amazon VPC by using an AWS PrivateLink endpoint

Amazon SageMaker now supports AWS PrivateLink for notebook instances. In this post, I will show you how to set up AWS PrivateLink to secure your connection to Amazon SageMaker notebooks. Maintaining compliance with regulations such as HIPAA or PCI may require preventing information from traversing the internet. Additionally, preventing exposure of data to the public internet reduces the likelihood […]

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Customize your notebook volume size, up to 16 TB, with Amazon SageMaker

Amazon SageMaker now allows you to customize the notebook storage volume when you need to store larger amounts of data. Allocating the right storage volume for your notebook instance is important while you develop machine learning models. You can use the storage volume to locally process a large dataset or to temporarily store other data to work with. […]

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Lifecycle configuration update for Amazon SageMaker notebook instances

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 […]

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Now use Pipe mode with CSV datasets for faster training on Amazon SageMaker built-in algorithms

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 […]

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Model Server for Apache MXNet v1.0 released

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 […]

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Using deep learning on AWS to lower property damage losses from natural disasters

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 […]

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Amazon Translate now offers 113 new language pairs

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 […]

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Understanding Amazon SageMaker notebook instance networking configurations and advanced routing options

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 has a variety of networking configurations […]

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Amazon SageMaker Batch Transform now supports Amazon VPC and AWS KMS-based encryption

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 […]

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