AWS Machine Learning Blog

Run text generation with Bloom and GPT models on Amazon SageMaker JumpStart

Today, we announce that large language models Bloom and GPT-2 are available in SageMaker JumpStart. Amazon SageMaker JumpStart is the machine learning hub of SageMaker that provides hundreds of built-in algorithms, pre-trained models, and end-to-end solution templates to help customers quickly get started with machine learning (ML). You can use these models for a wide […]

Deploy BLOOM-176B and OPT-30B on Amazon SageMaker with large model inference Deep Learning Containers and DeepSpeed

April 2023: This post was reviewed and updated for accuracy. The last few years have seen rapid development in the field of deep learning. Although hardware has improved, such as with the latest generation of accelerators from NVIDIA and Amazon, advanced machine learning (ML) practitioners still regularly encounter issues deploying their large deep learning models […]

Use Github Samples with Amazon SageMaker Data Wrangler

Amazon SageMaker Data Wrangler is a UI-based data preparation tool that helps perform data analysis, preprocessing, and visualization with features to clean, transform, and prepare data faster. Data Wrangler pre-built flow templates help make data preparation quicker for data scientists and machine learning (ML) practitioners by helping you accelerate and understand best practice patterns for […]

Transfer learning for TensorFlow object detection models in Amazon SageMaker

July 2023: You can also use the newly launched JumpStart APIs, an extension of the SageMaker Python SDK. These APIs allow you to programmatically deploy and fine-tune a vast selection of JumpStart-supported pre-trained models on your own datasets. Please refer to Amazon SageMaker JumpStart models and algorithms now available via API for more details on how […]

Transfer learning for TensorFlow text classification models in Amazon SageMaker

July 2023: You can also use the newly launched JumpStart APIs, an extension of the SageMaker Python SDK. These APIs allow you to programmatically deploy and fine-tune a vast selection of JumpStart-supported pre-trained models on your own datasets. Please refer to Amazon SageMaker JumpStart models and algorithms now available via API for more details on how […]

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Intelligent document processing with AWS AI and Analytics services in the insurance industry: Part 2

In Part 1 of this series, we discussed intelligent document processing (IDP), and how IDP can accelerate claims processing use cases in the insurance industry. We discussed how we can use AWS AI services to accurately categorize claims documents along with supporting documents. We also discussed how to extract various types of documents in an […]

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Intelligent document processing with AWS AI services in the insurance industry: Part 1

The goal of intelligent document processing (IDP) is to help your organization make faster and more accurate decisions by applying AI to process your paperwork. This two-part series highlights the AWS AI technologies that insurance companies can use to speed up their business processes. These AI technologies can be used across insurance use cases such […]

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Improving stability and flexibility of ML pipelines at Amazon Packaging Innovation with Amazon SageMaker Pipelines

To delight customers and minimize packaging waste, Amazon must select the optimal packaging type for billions of packages shipped every year. If too little protection is used for a fragile item such as a coffee mug, the item will arrive damaged and Amazon risks their customer’s trust. Using too much protection will result in increased […]

Improve data extraction and document processing with Amazon Textract

Intelligent document processing (IDP) has seen widespread adoption across enterprise and government organizations. Gartner estimates the IDP market will grow more than 100% year over year, and is projected to reach $4.8 billion in 2022. IDP helps transform structured, semi-structured, and unstructured data from a variety of document formats into actionable information. Processing unstructured data […]

Automated exploratory data analysis and model operationalization framework with a human in the loop

Identifying, collecting, and transforming data is the foundation for machine learning (ML). According to a Forbes survey, there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. In addition, many of our customers face several challenges during the model operationalization phase […]