Remove Automation Remove Explainability Remove IDP Remove ML
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Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

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Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics. With this new feature, you can use your own identity provider (IdP) such as Okta , Azure AD , or Ping Federate to connect to Snowflake via Data Wrangler. Configure Snowflake.

IDP 100
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Intelligent document processing with Amazon Textract, Amazon Bedrock, and LangChain

AWS Machine Learning Blog

Document processing has witnessed significant advancements with the advent of Intelligent Document Processing (IDP). With IDP, businesses can transform unstructured data from various document types into structured, actionable insights, dramatically enhancing efficiency and reducing manual efforts.

IDP 107
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Intelligent Document Processing with AWS AI Services and Amazon Bedrock

ODSC - Open Data Science

While the industry has been able to achieve some amount of automation through traditional OCR tools, these methods have proven to be brittle, expensive to maintain, and add to technical debt. The following diagram is how we visualize these IDP phases. This is a very common situation in ML workloads as business processes evolve.

IDP 98
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Dialogue-guided intelligent document processing with foundation models on Amazon SageMaker JumpStart

AWS Machine Learning Blog

Intelligent document processing (IDP) is a technology that automates the processing of high volumes of unstructured data, including text, images, and videos. Natural language processing (NLP) is one of the recent developments in IDP that has improved accuracy and user experience.

IDP 71
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Onboard users to Amazon SageMaker Studio with Active Directory group-specific IAM roles

AWS Machine Learning Blog

Amazon SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. For provisioning Studio in your AWS account and Region, you first need to create an Amazon SageMaker domain—a construct that encapsulates your ML environment.

IDP 68
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New – No-code generative AI capabilities now available in Amazon SageMaker Canvas

AWS Machine Learning Blog

Launched in 2021, Amazon SageMaker Canvas is a visual, point-and-click service that allows business analysts and citizen data scientists to use ready-to-use machine learning (ML) models and build custom ML models to generate accurate predictions without the need to write any code.

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Transform, analyze, and discover insights from unstructured healthcare data using Amazon HealthLake

AWS Machine Learning Blog

The rapid rate of data generation means that organizations that aren’t investing in document automation risk getting stuck with legacy processes that are manual, slow, error prone, and difficult to scale. Users can make predictions with health data using Amazon SageMaker ML models.