AWS Machine Learning Blog

Category: Amazon ML Solutions Lab

Create, train, and deploy a billion-parameter language model on terabytes of data with TensorFlow and Amazon SageMaker

The increasing size of language models has been one of the biggest trends in natural language processing (NLP) in recent years. Since 2018, we’ve seen unprecedented development and deployment of ever-larger language models, including BERT and its variants, GPT-2, T-NLG, and GPT-3 (175 billion parameters). These models have pushed the boundaries of possible architectural innovations. […]

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Build a custom Q&A dataset using Amazon SageMaker Ground Truth to train a Hugging Face Q&A NLU model

In recent years, natural language understanding (NLU) has increasingly found business value, fueled by model improvements as well as the scalability and cost-efficiency of cloud-based infrastructure. Specifically, the Transformer deep learning architecture, often implemented in the form of BERT models, has been highly successful, but training, fine-tuning, and optimizing these models has proven to be […]

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Build a custom entity recognizer for PDF documents using Amazon Comprehend

In many industries, it’s critical to extract custom entities from documents in a timely manner. This can be challenging. Insurance claims, for example, often contain dozens of important attributes (such as dates, names, locations, and reports) sprinkled across lengthy and dense documents. Manually scanning and extracting such information can be error-prone and time-consuming. Rule-based software […]

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Optimize customer engagement with reinforcement learning

This is a guest post co-authored by Taylor Names, Staff Machine Learning Engineer, Dev Gupta, Machine Learning Manager, and Argie Angeleas, Senior Product Manager at Ibotta. Ibotta is an American technology company that enables users with its desktop and mobile apps to earn cash back on in-store, mobile app, and online purchases with receipt submission, […]

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Automate digitization of transactional documents with human oversight using Amazon Textract and Amazon A2I

In this post, we present a solution for digitizing transactional documents using Amazon Textract and incorporate a human review using Amazon Augmented AI (A2I). You can find the solution source at our GitHub repository. Organizations must frequently process scanned transactional documents with structured text so they can perform operations such as fraud detection or financial […]

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Label text for aspect-based sentiment analysis using SageMaker Ground Truth

The Amazon Machine Learning Solutions Lab (MLSL) recently created a tool for annotating text with named-entity recognition (NER) and relationship labels using Amazon SageMaker Ground Truth. Annotators use this tool to label text with named entities and link their relationships, thereby building a dataset for training state-of-the-art natural language processing (NLP) machine learning (ML) models. Most […]

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Industrial automation at Tyson with computer vision, AWS Panorama, and Amazon SageMaker

This is the first in a two-part blog series on how Tyson Foods, Inc., is utilizing machine learning to automate industrial processes at their meat packing plants by bringing the benefits of artificial intelligence applications at the edge. In part one, we discuss an inventory counting application for packaging lines built using Amazon SageMaker and […]

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Train graph neural nets for millions of proteins on Amazon SageMaker and Amazon DocumentDB (with MongoDB compatibility)

There are over 180,000 unique proteins with 3D structures determined, with tens of thousands new structures resolved every year. This is only a small fraction of the 200 million known proteins with distinctive sequences. Recent deep learning algorithms such as AlphaFold can accurately predict 3D structures of proteins using their sequences, which help scale the […]

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AWS Deep Learning AMIs: New framework-specific DLAMIs for production complement the original multi-framework DLAMIs

Since its launch in November 2017, the AWS Deep Learning Amazon Machine Image (DLAMI) has been the preferred method for running deep learning frameworks on Amazon Elastic Compute Cloud (Amazon EC2). For deep learning practitioners and learners who want to accelerate deep learning in the cloud, the DLAMI comes pre-installed with AWS-optimized deep learning (DL) frameworks […]

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Clinical text mining using the Amazon Comprehend Medical new SNOMED CT API

Mining medical concepts from written clinical text, such as patient encounters, plays an important role in clinical analytics and decision-making applications, such as population analytics for providers, pre-authorization for payers, and adverse-event detection for pharma companies. Medical concepts contain medical conditions, medications, procedures, and other clinical events. Extracting medical concepts is a complicated process due […]

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