AWS Machine Learning Blog

Category: Artificial Intelligence

Training knowledge graph embeddings at scale with the Deep Graph Library

We’re extremely excited to share the Deep Graph Knowledge Embedding Library (DGL-KE), a knowledge graph (KG) embeddings library built on top of the Deep Graph Library (DGL). DGL is an easy-to-use, high-performance, scalable Python library for deep learning on graphs. You can now create embeddings for large KGs containing billions of nodes and edges two-to-five […]

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Safely deploying and monitoring Amazon SageMaker endpoints with AWS CodePipeline and AWS CodeDeploy

As machine learning (ML) applications become more popular, customers are looking to streamline the process for developing, deploying, and continuously improving models. To reliably increase the frequency and quality of this cycle, customers are turning to ML operations (MLOps), which is the discipline of bringing continuous delivery principles and practices to the data science team. […]

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Deploying your own data processing code in an Amazon SageMaker Autopilot inference pipeline

The machine learning (ML) model-building process requires data scientists to manually prepare data features, select an appropriate algorithm, and optimize its model parameters. It involves a lot of effort and expertise. Amazon SageMaker Autopilot removes the heavy lifting required by this ML process. It inspects your dataset, generates several ML pipelines, and compares their performance […]

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Multi-GPU and distributed training using Horovod in Amazon SageMaker Pipe mode

There are many techniques to train deep learning models with a small amount of data. Examples include transfer learning, few-shot learning, or even one-shot learning for an image classification task and fine-tuning for language models based on a pre-trained BERT or GPT2 model. However, you may still have a use case in which you need […]

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Building machine learning workflows with Amazon SageMaker Processing jobs and AWS Step Functions

Machine learning (ML) workflows orchestrate and automate sequences of ML tasks, including data collection, training, testing, evaluating an ML model, and deploying the models for inference. AWS Step Functions automates and orchestrates Amazon SageMaker-related tasks in an end-to-end workflow. The AWS Step Functions Data Science Software Development Kit (SDK) is an open-source library that allows […]

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Improving speech-to-text transcripts from Amazon Transcribe using custom vocabularies and Amazon Augmented AI

Businesses and organizations are increasingly using video and audio content for a variety of functions, such as advertising, customer service, media post-production, employee training, and education. As the volume of multimedia content generated by these activities proliferates, businesses are demanding high-quality transcripts of video and audio to organize files, enable text queries, and improve accessibility […]

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This month in AWS Machine Learning: July 2020 edition

Every day there is something new going on in the world of AWS Machine Learning—from launches to new use cases like posture detection to interactive trainings like the AWS Power Hour: Machine Learning on Twitch. We’re packaging some of the not-to-miss information from the ML Blog and beyond for easy perusing each month. Check back […]

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Enhancing recommendation filters by filtering on item metadata with Amazon Personalize

We’re pleased to announce enhancements to recommendation filters in Amazon Personalize, which provide you greater control on recommendations your users receive by allowing you to exclude or include items to recommend based on criteria that you define. For example, when recommending products for your e-retail store, you can exclude unavailable items from recommendations. If you’re […]

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Code-free machine learning: AutoML with AutoGluon, Amazon SageMaker, and AWS Lambda

One of AWS’s goals is to put machine learning (ML) in the hands of every developer. With the open-source AutoML library AutoGluon, deployed using Amazon SageMaker and AWS Lambda, we can take this a step further, putting ML in the hands of anyone who wants to make predictions based on data—no prior programming or data […]

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