Remove content tag analytics-lifecycle
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5 Best AI Document Management Solutions (April 2024)

Unite.AI

M-Files M-Files is an intelligent information management platform that offers a smarter approach to managing content throughout its entire lifecycle. The platform provides a comprehensive view of content across the organization without requiring a complex and expensive migration to a single repository.

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The Power of Self-Managed Content

Unite.AI

Many businesses today are floundering beneath the weight of rapidly generated content that hasn’t been properly contextualized or organized within their internal systems. The solution to this conundrum is self-managed content. Digital asset management provides a single source of truth for a business’ content.

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Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

Flipboard

Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics. Configure SageMaker Studio You store the fields and values in a Secrets Manager secret and add it to the Studio Lifecycle Configuration that you’re using for Data Wrangler. Choose Next.

IDP 100
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Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation

AWS Machine Learning Blog

At Deutsche Bahn, a dedicated AI platform team manages and operates the SageMaker Studio platform, and multiple data analytics teams within the organization use the platform to develop, train, and run various analytics and ML activities. These tags also help track associated costs for the domain.

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Accelerate ML workflows with Amazon SageMaker Studio Local Mode and Docker support

AWS Machine Learning Blog

Build a Docker image using the reference Dockerfile: docker build --network sagemaker --tag myflaskapp:v1 --file./Dockerfile. Re-tag the build as an ECR image and push. amazonaws.com/myflaskapp:v1 sagemaker-user@default:~$ docker image list REPOSITORY TAG IMAGE ID CREATED SIZE 123456789012.dkr.ecr.us-east-2.amazonaws.com/myflaskapp

ML 90
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Nielsen Sports sees 75% cost reduction in video analysis with Amazon SageMaker multi-model endpoints

AWS Machine Learning Blog

Nielsen Sports shapes the world’s media and content as a global leader in audience insights, data, and analytics. The following figure shows an example of our tagging system. Our solution automatically segments the broadcast and knows how to isolate the relevant video clips from the rest of the content.

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Best practices and design patterns for building machine learning workflows with Amazon SageMaker Pipelines

AWS Machine Learning Blog

xlarge, instance_count=1, base_job_name="sklearn-abalone-process", role=role, sagemaker_session=local_pipeline_session, ) Manage a SageMaker pipeline through versioning Versioning of artifacts and pipeline definitions is a common requirement in the development lifecycle. Meenakshisundaram Thandavarayan works for AWS as an AI/ ML Specialist.