AWS Machine Learning Blog

Category: Artificial Intelligence

Transcribe speech to text in real time using Amazon Transcribe with WebSocket

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications. In November 2018, we added streaming transcriptions over HTTP/2 to Amazon Transcribe. This enabled users to pass a live audio stream to our service and, in return, receive text transcripts in real time. We […]

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Using Amazon Polly in Windows Applications

AWS offers a vast array of services that allow developers to build applications in the cloud. At the same time, Windows desktop applications can take advantage of these services as well. Today, we are releasing Amazon Polly for Windows, an open-source engine that allows users to take advantage of Amazon Polly voices in SAPI-compliant Windows […]

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Build your ML skills with AWS Machine Learning on Coursera

Machine learning (ML) is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market. Today, I am excited to announce a new education course, built in collaboration with Coursera, to help you build your ML skills: Getting started with AWS Machine Learning. You can access the course […]

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Build, test, and deploy your Amazon Sagemaker inference models to AWS Lambda

Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. When you deploy an ML model, Amazon SageMaker leverages ML hosting instances to host the model and provides an API endpoint to provide inferences. It may also […]

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Multiregion serverless distributed training with AWS Batch and Amazon SageMaker

Creating a global footprint and access to scale are one of the many best practices at AWS. By creating architectures that take advantage of that scale and also efficient data utilization (in both performance and cost), you can start to see how important access is at scale. For example, within autonomous vehicles (AV) development, data is geographically […]

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Building a deep neural net–based surrogate function for global optimization using PyTorch on Amazon SageMaker

Optimization is the process of finding the minimum (or maximum) of a function that depends on some inputs, called design variables. Customer X has the following problem: They are about to release a new car model to be designed for maximum fuel efficiency. In reality, thousands of parameters that represent tuning parameters relating to the […]

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Launching TensorFlow distributed training easily with Horovod or Parameter Servers in Amazon SageMaker

Amazon SageMaker supports all the popular deep learning frameworks, including TensorFlow. Over 85% of TensorFlow projects in the cloud run on AWS. Many of these projects already run in Amazon SageMaker. This is due to the many conveniences Amazon SageMaker provides for TensorFlow model hosting and training, including fully managed distributed training with Horovod and […]

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Performing batch inference with TensorFlow Serving in Amazon SageMaker

After you’ve trained and exported a TensorFlow model, you can use Amazon SageMaker to perform inferences using your model. You can either: Deploy your model to an endpoint to obtain real-time inferences from your model. Use batch transform to obtain inferences on an entire dataset stored in Amazon S3. In the case of batch transform, […]

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Optimizing TensorFlow model serving with Kubernetes and Amazon Elastic Inference

This post offers a dive deep into how to use Amazon Elastic Inference with Amazon Elastic Kubernetes Service. When you combine Elastic Inference with EKS, you can run low-cost, scalable inference workloads with your preferred container orchestration system. Elastic Inference is an increasingly popular way to run low-cost inference workloads on AWS. It allows you […]

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Tracking the throughput of your private labeling team through Amazon SageMaker Ground Truth

Launched at AWS re:Invent 2018, Amazon SageMaker Ground Truth helps you quickly build highly accurate training datasets for your machine learning models. Amazon SageMaker Ground Truth offers easy access to public and private human labelers, and provides them with built-in workflows and interfaces for common labeling tasks. Additionally, Amazon SageMaker Ground Truth can lower your […]

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