Remove Accountability Remove Big data Remove Enterprise
article thumbnail

Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

The data mesh architecture aims to increase the return on investments in data teams, processes, and technology, ultimately driving business value through innovative analytics and ML projects across the enterprise. The diagram shows several accounts and personas as part of the overall infrastructure.

article thumbnail

Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

An AWS account and an AWS Identity and Access Management (IAM) principal with sufficient permissions to create and manage the resources needed for this application. If you don’t have an AWS account, refer to How do I create and activate a new Amazon Web Services account? The script deploys the AWS CDK project in your account.

APIs 124
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

MLOps foundation roadmap for enterprises with Amazon SageMaker

AWS Machine Learning

As enterprise businesses embrace machine learning (ML) across their organizations, manual workflows for building, training, and deploying ML models tend to become bottlenecks to innovation. Building an MLOps foundation that can cover the operations, people, and technology needs of enterprise customers is challenging.

article thumbnail

Digital Transformation in Retail Banks: Potential Impact on Brand Equity, Customers, and Employees

Beyond Philosophy

I’m capitalizing the first letter of each word because the pervasiveness of digital transformation has all the feel of Big Data a few years ago and Reeingineering in the 1990’s. Much of the digital transformation emphasis has been on technology (big data analytics and cloud, mobile apps, etc.)

Banking 225
article thumbnail

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

article thumbnail

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.

article thumbnail

Set up cross-account Amazon S3 access for Amazon SageMaker notebooks in VPC-only mode using Amazon S3 Access Points

AWS Machine Learning

Typically, these datasets are aggregated in a centralized Amazon Simple Storage Service (Amazon S3) location from various business applications and enterprise systems. This post walks through the steps involved in configuring S3 Access Points to enable cross-account access from a SageMaker notebook instance.