AWS Machine Learning Blog

Category: Artificial Intelligence

Bring your own deep learning framework to Amazon SageMaker with Model Server for Apache MXNet

Deep learning (DL) frameworks enable machine learning (ML) practitioners to build and train ML models. However, the process of deploying ML models in production to serve predictions (also known as inferences) in real time is more complex. It requires that ML practitioners build a scalable and performant model server, which can host these models and […]

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Build a custom vocabulary to enhance speech-to-text transcription accuracy with Amazon Transcribe

Amazon Transcribe is a fully-managed automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capabilities to applications. Depending on your use case, you may have domain-specific terminology that doesn’t transcribe properly (e.g. “EBITDA” or “myocardial infarction”). In this post, we will show you how to leverage the custom vocabulary feature […]

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A personalized ‘shop-by-style’ experience using PyTorch on Amazon SageMaker and Amazon Neptune

Remember the screech of the dial-up and plain-text websites? It was in that era that the Amazon.com website launched in the summer of 1995. Like the rest of the web, Amazon.com has gone through a digital experience makeover that includes slick web controls, rich media, multi-channel support, and intelligent content placement. Nonetheless, there are certain […]

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Deploying PyTorch inference with MXNet Model Server

Training and inference are crucial components of a machine learning (ML) development cycle. During the training phase, you teach a model to address a specific problem. Through this process, you obtain binary model files ready for use in production. For inference, you can choose among several framework-specific solutions for model deployment, such as TensorFlow Serving […]

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Associating prediction results with input data using Amazon SageMaker Batch Transform

When you run predictions on large datasets, you may want to drop some input attributes before running the predictions. This is because those attributes don’t carry any signal or were not part of the dataset used to train your machine learning (ML) model. Similarly, it can be helpful to map the prediction results to all […]

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Support for Apache MXNet 1.4 and Model Server in Amazon SageMaker

Apache MXNet is an open-source deep learning software framework used to train and deploy deep neural networks. Data scientists and machine learning (ML) developers love MXNet due to its flexibility and efficiency when building deep learning models. Amazon SageMaker is committed to improving the customer experience for all ML frameworks and libraries, including MXNet. With the latest release of […]

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The AWS DeepRacer League visits Hong Kong, bringing together developers of all skill levels!

The AWS DeepRacer League is the world’s first global autonomous racing league, open to anyone. Developers of all skill levels can compete in person at 22 AWS events globally, or online via the AWS DeepRacer console (no car required), for a chance to win an expenses paid trip to re:Invent 2019, where they will race […]

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Empowering wheelchair users with a socially assistive robot running on Amazon Machine Learning

Loro is a socially assistive robot that helps users with physical limitations to more robustly experience their worlds by assisting with seeing, sensing, speaking, and interacting with surroundings.  Loro uses a range of AWS artificial intelligence (AI) and especially machine learning (ML) services to enable its broad range of use cases. Wheelchair users and others […]

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Amazon SageMaker Ground Truth: Using A Pre-Trained Model for Faster Data Labeling

With Amazon SageMaker Ground Truth, you can build highly accurate training datasets for machine learning quickly. 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, SageMaker Ground Truth can lower your labeling costs by up to 70% using automatic labeling, […]

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