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Favorite This post is a collaboration between AWS, NVIDIA and Heidi. Reducing automatic speech recognition (ASR) inference costs on Amazon Elastic Compute Cloud (Amazon EC2) becomes critical when GPU utilization per request is low but latency requirements are strict. A single ASR inference request typically uses only 15–20 percent of a
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Shared by AWS Machine Learning August 28, 2026
Favorite Self-hosted speech AI has historically carried an observability trade-off. The service can tell you an endpoint is up and how many requests it served. The questions that actually drive capacity planning and cost management stay locked inside the vendor’s container: what you are billed for, which features your traffic
Read More Shared by AWS Machine Learning August 28, 2026
Favorite Amazon Bedrock now supports the OpenAI GPT-5.6 models, Terra and Luna, in India, with India geographic cross-Region inference. If you have local data processing requirements in India, including in financial services, healthcare, and the public sector, you can now use these OpenAI models at scale. Amazon Bedrock processes inference
Read More Shared by AWS Machine Learning August 28, 2026
Favorite Creative teams face growing demand for more assets, formats, and revisions, while their scripts, references, models, and outputs often remain fragmented across tools. Creators must repeatedly transfer context and assemble results manually. With 78% of creative leaders saying demand exceeds their teams’ capacity, faster generation alone does not solve
Read More Shared by AWS Machine Learning August 28, 2026
Favorite Book hotels and track airfares, plus view miles and rewards with AI Mode in Google Search. View Original Source (blog.google/technology/ai/) Here.
Favorite Organizations often deploy agents using Amazon Bedrock AgentCore, a platform to build, connect, and optimize agents at scale, with any framework or model. These agents may access governed knowledge bases hosted in separate AWS accounts. This cross-account separation helps maintain clear workload boundaries but can introduce integration challenges. This
Read More Shared by AWS Machine Learning August 27, 2026
Favorite Data preparation determines the ceiling of any supervised fine-tuning (SFT) project. You’ve evaluated your foundation model (FM), and out-of-the-box performance isn’t meeting your production requirements. Maybe the model doesn’t follow your output schema reliably, struggles with your domain’s classification taxonomy, or can’t maintain the tone your application demands. The
Read More Shared by AWS Machine Learning August 27, 2026
Favorite Data preparation for supervised fine-tuning (SFT) doesn’t end when your dataset is clean and correctly formatted. The harder questions come next. How much data do you actually need? Should you collect more, or select a better subset of what you have? How do you generate high-quality examples when human
Read More Shared by AWS Machine Learning August 27, 2026
Favorite In 2021, we published Bring your own model with Amazon SageMaker script mode. That post showed how to use script mode on managed framework containers from AWS to write custom training and inference code. Script mode was a leap forward: you didn’t need to build or maintain Docker images
Read More Shared by AWS Machine Learning August 27, 2026
Favorite Booking a phlebotomy appointment shouldn’t be a hassle for oncology patients already managing treatment. Natera’s service, powered by Amazon Bedrock AgentCore, allows a phlebotomist to come to the patient, helping Natera deliver a more convenient experience. Natera, a global diagnostics company specializing in cell-free DNA testing, wanted to transform
Read More Shared by AWS Machine Learning August 27, 2026