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Favorite Deploying a Hugging Face model to production means making a dozen decisions: choosing the right serving container for the model’s architecture, confirming the current image tag for your AWS Region, and matching an instance type to the model’s memory footprint. Beyond infrastructure, you must wire autoscaling so you don’t
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Shared by AWS Machine Learning September 19, 2026
Favorite Agents are no longer experiments. They process claims, write and review code, coordinate across systems, and run for hours without supervision. As agents take on more complex, longer-running work, the infrastructure underneath them must evolve just as fast. We built Amazon Bedrock AgentCore to help developers build, connect, and
Read More Shared by AWS Machine Learning September 19, 2026
Favorite Organizations building multi-model agentic AI applications face growing infrastructure complexity. Managing container orchestration, scaling policies, identity, and observability for multiple model types adds operational overhead. Teams often spend more time on infrastructure than on agent logic development. Developers running agentic frameworks on self-managed infrastructure such as Amazon Elastic Container
Read More Shared by AWS Machine Learning September 19, 2026
Favorite Open-weight models are changing the economics of building and deploying AI at scale. Rapid gains in intelligence and efficiency mean companies can match each workload with the right balance of capability, speed, and cost. AWS is building for a future in which organizations can adopt open-weight innovation with the
Read More Shared by AWS Machine Learning September 19, 2026
Favorite Generative AI inference is uniquely hard: models are tens to hundreds of gigabytes, latency requirements are measured in tokens per second, cold starts can span multiple minutes as containers and weights transfer, GPU capacity is constrained, and traditional monitoring tools expose none of the token-level signals that matter in
Read More Shared by AWS Machine Learning September 19, 2026
Favorite Industrial safety AI refers to the use of technologies like computer vision and predictive analytics to find and stop workplace dangers. Synthetic data augmentation is emerging as a practical solution to one of the hardest problems in industrial safety AI: the scarcity of training images depicting people in dangerous
Read More Shared by AWS Machine Learning September 18, 2026
Favorite Each Model Context Protocol (MCP) tool invocation on Amazon Quick is an access event that can require defense-in-depth authorization at the tool and parameter level. This applies in addition to a valid token. Without granular controls, a single misconfigured permission can bypass the access requirements that organizations might need
Read More Shared by AWS Machine Learning September 18, 2026
Favorite Basic AI chat isn’t enough for financial services organizations that need secure, self-service AI agents. In financial services, employees need AI that can work with internal systems and sensitive client data, stay inside a governed environment, and remain auditable and cost-transparent. All of this must happen without every team
Read More Shared by AWS Machine Learning September 18, 2026
Favorite Building a working agentic prototype takes an afternoon. Getting it to production is where the work explodes. The moment an agent has to serve more than one user, a new layer of engineering appears and it’s critical to tell whether the agent is doing the right thing on real
Read More Shared by AWS Machine Learning September 18, 2026
Favorite Git activity is one of the richest signals engineering teams produce that can provide continuous observability into development analytics. The challenge is extracting these Git metrics at scale, which has traditionally required hand-rolled extract, transform, and load (ETL) jobs, dedicated infrastructure, and ongoing maintenance. Further, with modern development tools
Read More Shared by AWS Machine Learning September 18, 2026