AWS Machine Learning Blog

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We released an update to Amazon Comprehend enabling support for private, custom entity types. Customers can now train state-of-the-art entity recognition models to extract their specific terms, completely automatically. No machine learning experience required. For example, financial […]

Amazon Polly is an AWS service that turns text into lifelike speech. This pre-trained service requires no machine learning skills to easily integrate AI into your applications. In addition to the previously available Italian voices Carla and Giorgio, we have now added a second female Italian voice. Listen to the introduction by Bianca. Listen now Voiced […]

In this blog post, we’re introducing the Amazon SageMaker Object2Vec algorithm, a new highly customizable multi-purpose algorithm that can learn low dimensional dense embeddings of high dimensional objects. Embeddings are an important feature engineering technique in machine learning (ML). They convert high dimensional vectors into low-dimensional space to make it easier to do machine learning […]

Amazon SageMaker provides several built-in machine learning (ML) algorithms that you can use for a variety of problem types. These algorithms provide high-performance, scalable machine learning and are optimized for speed, scale, and accuracy. Using these algorithms you can train on petabyte-scale data. They are designed to provide up to 10x the performance of the other […]

Today, AWS announced that Amazon Translate, Amazon Comprehend, and Amazon Transcribe are now U.S. Health Insurance Portability and Accountability Act of 1996 (HIPAA) eligible services. This announcement adds to the number of AWS artificial intelligence services that are already HIPAA eligible– Amazon Polly, Amazon SageMaker, and Amazon Rekognition. By using these services, AWS customers in […]

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 […]

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 […]

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. […]

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 […]