Favorite We are excited to announce Amazon Transcribe Medical, a new HIPAA-eligible, machine learning automatic speech recognition (ASR) service that allows developers to add medical speech-to-text capabilities to their applications. Transcribe Medical provides accurate and affordable medical transcription, enabling healthcare providers, IT vendors, insurers, and pharmaceutical companies to build services

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Shared by AWS Machine Learning December 4, 2019

Favorite Two of the most popular machine learning models used today are BERT, for natural language processing (NLP), and Mask R-CNN, for image recognition. Over the past several months, AWS has significantly improved the underlying infrastructure, network, machine learning (ML) framework, and model code to achieve the best training time

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Shared by AWS Machine Learning December 4, 2019

Favorite Since the launch of AWS DeepRacer, tens of thousands of developers from around the world have been getting hands-on experience with reinforcement learning in the AWS Management Console, by building their AWS DeepRacer models and competing in the AWS DeepRacer League for a chance to be crowned the 2019

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Shared by AWS Machine Learning December 2, 2019

Favorite AWS is excited to introduce Amazon SageMaker Operators for Kubernetes, a new capability that makes it easier for developers and data scientists using Kubernetes to train, tune, and deploy machine learning (ML) models in Amazon SageMaker. Customers can install these Amazon SageMaker Operators on their Kubernetes cluster to create

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Shared by AWS Machine Learning December 2, 2019

Favorite We are happy to announce that Amazon Forecast can now generate forecasts at a quantile of your choice. Launched at re:Invent 2018, and generally available since Aug 2019, Forecast is a fully managed service that uses machine learning (ML) to generate highly accurate forecasts, without requiring any prior ML

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Shared by AWS Machine Learning November 27, 2019

Favorite Today AWS announced new ways for you to easily add machine learning (ML) predictions to applications and business intelligence (BI) dashboards using relational data in your Amazon Aurora database and unstructured data in Amazon S3, by simply adding a few statements to your SQL (structured query language) queries and

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Shared by AWS Machine Learning November 26, 2019

Favorite Organizations in all industries have a large number of physical documents. It can be difficult to extract text from a scanned document when it contains formats such as tables, forms, paragraphs, and check boxes. Organizations have been addressing these problems with Optical Character Recognition (OCR) technology, but it requires

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Shared by AWS Machine Learning November 26, 2019

Favorite Machine learning (ML) is routinely used in every sector to make predictions. But beyond simple predictions, making decisions is more complicated because non-optimal short-term decisions are sometimes preferred or even necessary to enable long-term, strategic goals. Optimizing policies to make sequential decisions toward a long-term objective can be learned

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Shared by AWS Machine Learning November 26, 2019

Favorite Businesses are increasingly developing per-user machine learning (ML) models instead of cohort or segment-based models. They train anywhere from hundreds to hundreds of thousands of custom models based on individual user data. For example, a music streaming service trains custom models based on each listener’s music history to personalize

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Shared by AWS Machine Learning November 26, 2019

Favorite Today, Amazon Web Services (AWS) announced Amazon Rekognition Custom Labels, a new feature of Amazon Rekognition that enables customers to build their own specialized machine learning (ML) based image analysis capabilities to detect unique objects and scenes integral to their specific use case. For example, customers using Amazon Rekognition

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Shared by AWS Machine Learning November 25, 2019