AWS now enables extensive customization of Amazon Nova foundation models through SageMaker AI with techniques including continued pre-training, supervised fine-tuning, direct preference optimization,…

    Amazon SageMaker AI cuts generative AI inference scale-out time by up to half with automatic container image caching 0 views Eyal Estrin unread, 3:44 PM (17 minutes ago)    to https://aws.amazon.com/about-aws/whats-new/2026/06/sagemakerai-inf-scale-out-time/ https://aws.amazon.com/blogs/machine-learning/introducing-container-caching-in-amazon-sagemaker-ai-for-faster-model-scaling/ Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

Self-hosted speech AI carries an observability trade-off: the numbers that drive capacity planning and cost management stay locked inside the vendor container. Deepgram closes that gap on Amazon SageMaker AI with two capabilities that land billing, usage, and per-GPU metrics directly in your own Amazon CloudWatch account.

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🚀 Amazon SageMaker AI unterstützt nun optimierte generative AI-Inference! Validierte Konfigs mit Metrics – Entwickler bauen Modelle, nicht Infra. Wil...

Observe.ai developed the One Load Audit Framework (OLAF), which integrates with SageMaker to identify bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static and dynamic data loads. In this blog post, you will learn how to use the OLAF utility to test and validate your SageMaker endpoint.

Observe.ai's OLAF framework integrates with Amazon SageMaker to streamline load testing of ML inference endpoints, reducing testing time from weeks to hours and enabling faster deployment and scaling of AI services.

AWS launches Amazon SageMaker AI Inference for custom Amazon Nova models. You can now configure the instance types, auto-scaling policies, and concurrency settings for custom Nova model deployments to...

Lumi is a leading Australian fintech lender empowering small businesses with fast, flexible, and transparent funding solutions. They use real-time data and machine learning (ML) to offer customized loans...

Responsible AI: How PowerSchool safeguards millions of students with AI-powered content filtering using Amazon SageMaker AIArtificial Intelligence In this post, we demonstrate how PowerSchool built an...

Eyal Estrin unread, 12:30 AM (30 minutes ago)    to https://aws.amazon.com/blogs/machine-learning/speed-meets-scale-load-testing-sagemakerai-endpoints-with-observe-ais-testing-tool/ Eyal Estrin CISSP, CCSP, CISM, CISA, CDPSE, CCSK Blog: https://security-24-7.com | Books: https://amzn.to/42Xai9A | https://amzn.to/3Sggbtv Twitter: @eyalestrin | Bluesky: @eyalestrin.bsky.social

Learn how to load test Amazon SageMakerAI endpoints at scale using Observe.AI’s open‑source OLAF framework, optimize latency and throughput, and cut ML inference costs with data‑driven capacity planning.

Observe.ai developed the One Load Audit Framework (OLAF), which integrates with SageMaker to identify bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static and dynamic data loads. In this blog post, you will learn how to use the OLAF utility to test and validate your SageMaker endpoint.