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Unlocking efficiency: Harnessing the power of Selective Execution in Amazon SageMaker Pipelines | Amazon Web Services

MLOps is a key discipline that often oversees the path to productionizing machine learning (ML) models. It’s natural to focus on a single model...

MoustachedBouncer: Espionage against foreign diplomats in Belarus

MoustachedBouncer is a cyberespionage group discovered by ESET Research and first publicly disclosed in this blogpost. The group has been active since at least...

Generate creative advertising using generative AI deployed on Amazon SageMaker | Amazon Web Services

Creative advertising has the potential to be revolutionized by generative AI (GenAI). You can now create a wide variation of novel images, such as...

Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce apps with AI/ML | Amazon Web Services

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. This is the second post in a series discussing the integration...

Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics | Amazon Web Services

If you are a business analyst, understanding customer behavior is probably one of the most important things you care about. Understanding the reasons and...

Configure cross-account access of Amazon Redshift clusters in Amazon SageMaker Studio using VPC peering | Amazon Web Services

With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is...

Build machine learning-ready datasets from the Amazon SageMaker offline Feature Store using the Amazon SageMaker Python SDK | Amazon Web Services

Amazon SageMaker Feature Store is a purpose-built service to store and retrieve feature data for use by machine learning (ML) models. Feature Store provides...

Operationalize ML models built in Amazon SageMaker Canvas to production using the Amazon SageMaker Model Registry

You can now register machine learning (ML) models built in Amazon SageMaker Canvas with a single click to the Amazon SageMaker Model Registry, enabling...

Amazon SageMaker with TensorBoard: An overview of a hosted TensorBoard experience

Today, data scientists who are training deep learning models need to identify and remediate model training issues to meet accuracy targets for production deployment,...

Accelerate protein structure prediction with the ESMFold language model on Amazon SageMaker

Proteins drive many biological processes, such as enzyme activity, molecular transport, and cellular support. The three-dimensional structure of a protein provides insight into its...

Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even...

Recommend top trending items to your users using the new Amazon Personalize recipe

Amazon Personalize is excited to announce the new Trending-Now recipe to help you recommend items gaining popularity at the fastest pace among your users....

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