AWS Machine Learning Blog

Category: Artificial Intelligence

AWS IQ waives fees until June 30, 2020, to help you stand up and scale remote work initiatives

The recent post Working from Home? Here’s How AWS Can Help shared several ways AWS is helping you set up and scale remote work and work-from-home initiatives. Getting these solutions set up is sometimes best—and achieved more quickly—with expert help. You can get the help you need with AWS IQ, which connects you to AWS […]

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Spanish Newscaster speaking style now available in Amazon Polly

Amazon Polly is launching its first non-English Newscaster speaking style voice, Lupe. This female US Spanish voice is the third Newscaster speaking style voice in Amazon Polly, following the launches of US English voices Matthew and Joanna. All Newscaster voices are made possible by the Neural Text-to-Speech (NTTS) technology of Amazon Polly. News content is […]

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Reduce inference costs on Amazon EC2 for PyTorch models with Amazon Elastic Inference

You can now use Amazon Elastic Inference to accelerate inference and reduce inference costs for PyTorch models in both Amazon SageMaker and Amazon EC2. PyTorch is a popular deep learning framework that uses dynamic computational graphs. This allows you to easily develop deep learning models with imperative and idiomatic Python code. Inference is the process […]

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Amazon Translate added to Memsource Translate, Memsource’s machine translation management feature

This is a guest post from Memsource. In their own words, “By leading the industry in AI-powered translation technology, we make localization easier, faster, and more cost-effective.” Memsource and Amazon Translate are strengthening their partnership. You can now use Amazon Translate with Memsource Translate, Memsource’s machine translation (MT) management feature. Many Memsource users share a […]

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Bring your own model for Amazon SageMaker labeling workflows with active learning

With Amazon SageMaker Ground Truth, you can easily and inexpensively build accurately labeled machine learning (ML) datasets. To decrease labeling costs, SageMaker Ground Truth uses active learning to differentiate between data objects (like images or documents) that are difficult and easy to label. Difficult data objects are sent to human workers to be annotated and […]

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Optimizing Japanese text-to-speech with Amazon Polly

Amazon Polly is a cloud service that offers text-to-speech (TTS), a system that converts text input into a waveform, in a range of 61 voices in 29 languages by using advanced deep learning technologies. The Amazon Polly service supports companies in developing digital products that use speech synthesis for a variety of use cases, including […]

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Introducing recommendation scores in Amazon Personalize

Amazon Personalize enables you to personalize your website, app, ads, emails, and more, using the same machine learning technology as used by Amazon.com, without requiring any prior machine learning experience. Using Amazon Personalize, you can generate personalized recommendations for your users through a simple API interface. We are pleased to announce that Amazon Personalize now […]

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Deploying machine learning models as serverless APIs

Machine learning (ML) practitioners gather data, design algorithms, run experiments, and evaluate the results. After you create an ML model, you face another problem: serving predictions at scale cost-effectively. Serverless technology empowers you to serve your model predictions without worrying about how to manage the underlying infrastructure. Services like AWS Lambda only charge for the […]

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Reducing player wait time and right sizing compute allocation using Amazon SageMaker RL and Amazon EKS

As a multiplayer game publisher, you may often need to either over-provision resources or manually manage compute allocation when launching or maintaining an online game to avoid long player wait times. You need to develop, configure, and deploy tools that help you monitor and control the compute allocation. This post demonstrates GameServer Autopilot, a new […]

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Autodesk optimizes visual similarity search model in Fusion 360 with Amazon SageMaker Debugger

This post is co-written by Alexander Carlson, a machine learning engineer at Autodesk. Autodesk started its digital transformation journey years ago by moving workloads from private data centers to AWS services. The benefits of digital transformation are clear with generative design, which is a new technology that uses cloud computing to accelerate design exploration beyond […]

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