Exploring the zero operator access design of Mantle

Why it matters (2 min)

• At Amazon, our culture, built on honest and transparent discussion of our growth opportunities, enables us to focus on investing and innovating to continually raise the standard on our ability to…
• Earlier this month, we had the opportunity to share an example of this process at work in Mantle, our next-generation inference engine for Amazon Bedrock .
• Open receipts to verify and go deeper.

Go deeper (8 min)

Context

At Amazon, our culture, built on honest and transparent discussion of our growth opportunities, enables us to focus on investing and innovating to continually raise the standard on our ability to deliver value for our customers. Earlier this month, we had the opportunity to share an example of this process at work in Mantle, our next-generation inference engine for Amazon Bedrock . As generative AI inferencing and fine-tuning workloads continue to evolve, we need to evolve how we serve inferencing to our customers in an optimized way, which leads to the development of Mantle. As we set out to reimagine the architecture of our next generation inferencing engine, we made raising the bar on security our top priority. AWS shares our customers’ unwavering focus on security and data privacy. This has been central to our business from the start, and it was particularly in focus from the earliest days of Amazon Bedrock. We’ve understood from the start that generative AI inference workloads present an unprecedented opportunity for customers to harness the latent value of their data, but with that opportunity comes the need to ensure the highest standards in security, privacy, and…

For builders

Builder: read docs/changelog; watch for breaking changes, quotas, and pricing.

Verify

Prefer primary announcements, papers, repos, and changelogs over reposts.

Receipts

1. Exploring the zero operator access design of Mantle (AWS Machine Learning Blog)