Remove cross-elasticity
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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

In this post, we discuss some of the newer cross-account features to Amazon SageMaker that allow you to better share and manage model groups as well as manage model versions. Amazon Elastic Container Registry (Amazon ECR) is optional; it’s only required if your model requires its own environment. Define cross-account policies.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

BYOC involves containerizing the algorithm and registering the image in Amazon Elastic Container Registry (Amazon ECR) , and then using the same image to create a container to do training and inference. He holds the AWS AI/ML Specialty certification and authors technical blogs on AI/ML services and solutions.

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

Consider inserting AWS Web Application Firewall (AWS WAF) in front to protect web applications and APIs from malicious bots , SQL injection attacks, cross-site scripting (XSS), and account takeovers with Fraud Control.

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

This is a joint blog with AWS and Philips. In the following sections, we discuss the key capabilities of the platform enabled by AWS services, including SageMaker, AWS Service Catalog , CloudWatch, AWS Lambda , Amazon Elastic Container Registry (Amazon ECR), Amazon S3, AWS Identity and Access Management (IAM), and others.

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Enable data sharing through federated learning: A policy approach for chief digital officers

AWS Machine Learning

This is a guest blog post written by Nitin Kumar, a Lead Data Scientist at T and T Consulting Services, Inc. It also takes away potential high-level compute challenges with on-premises hardware with Amazon Elastic Compute Cloud (Amazon EC2) resources.

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Cohere Command R and R+ are now available in Amazon SageMaker JumpStart

AWS Machine Learning

This blog post is co-written with Pradeep Prabhakaran from Cohere. Each model can be deployed on Amazon Elastic Compute Cloud (EC2) P5 instances powered by NVIDIA H100 Tensor Core GPUs (p5.48xlarge) and Amazon EC2 P4de instances powered by NVIDIA A100 Tensor Core GPUs (ml.p4de.24xlarge).

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Build a crop segmentation machine learning model with Planet data and Amazon SageMaker geospatial capabilities

AWS Machine Learning

The number of neighbors, a parameter greatly affecting the estimator’s performance, is tuned using cross-validation in KNN cross-validation. Model training After the data has been downloaded with the Planet Python client, the segmentation model can be trained.

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