AWS Machine Learning Blog

SEPTEMBER 3, 2024

Scalability and performance – The EMR Serverless integration automatically scales the compute resources up or down based on your workload’s demands, making sure you always have the necessary processing power to handle your big data tasks. This same interface is also used for provisioning EMR clusters.

AWS 120

AWS Clustering Big Data Big Data 120

AWS Machine Learning Blog

AUGUST 22, 2023

With SageMaker, data scientists and developers can quickly build and train ML models, and then deploy them into a production-ready hosted environment. In this post, we demonstrate how to use the managed ML platform to provide a notebook experience environment and perform federated learning across AWS accounts, using SageMaker training jobs.

Machine Learning 98

Machine Learning Machine Learning AWS ML 98

Flipboard

MARCH 7, 2023

You can manage app images via the SageMaker console, the AWS SDK for Python (Boto3), and the AWS Command Line Interface (AWS CLI). The Studio Image Build CLI lets you build SageMaker-compatible Docker images directly from your Studio environments by using AWS CodeBuild. Environments without internet access.

Python 123

Python AWS ML ML 123

AWS Machine Learning Blog

MAY 10, 2023

Prerequisites The following prerequisites are needed to implement this solution: An AWS account with permissions to create AWS Identity and Access Management (IAM) policies and roles. About the Authors Ajjay Govindaram is a Senior Solutions Architect at AWS. Varun Mehta is a Solutions Architect at AWS.

ML 101

ML ML AWS Data Analysis 101