AWS Machine Learning Blog

Category: Featured

Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

Cloud costs can significantly impact your business operations. Gaining real-time visibility into infrastructure expenses, usage patterns, and cost drivers is essential. To allocate costs to cloud resources, a tagging strategy is essential. This post outlines steps you can take to implement a comprehensive tagging governance strategy across accounts, using AWS tools and services that provide visibility and control. By setting up automated policy enforcement and checks, you can achieve cost optimization across your machine learning (ML) environment.

Scaling Rufus, the Amazon generative AI-powered conversational shopping assistant with over 80,000 AWS Inferentia and AWS Trainium chips, for Prime Day

In this post, we dive into the Rufus inference deployment using AWS chips and how this enabled one of the most demanding events of the year—Amazon Prime Day.

Empowering everyone with GenAI to rapidly build, customize, and deploy apps securely: Highlights from the AWS New York Summit

See how AWS is democratizing generative AI with innovations like Amazon Q Apps to make AI apps from prompts, Amazon Bedrock upgrades to leverage more data sources, new techniques to curtail hallucinations, and AI skills training.

Amazon SageMaker now integrates with Amazon DataZone to streamline machine learning governance

Unlock ML governance with SageMaker-DataZone integration: streamline infrastructure, collaborate, and govern data/ML assets.