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GoDaddy is one of the world’s largest domain registrar and web hosting companies, serving more than 20 million customers and managing approximately 82 million domain names. At that scale, access to timely business data directly affects how quickly the company can act. When GoDaddy’s analytics infrastructure faced challenges under the weight of thousands of dashboards, […]

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 their patient experience by replacing manual

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 to run your own algorithm on

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 annotation doesn’t scale? And how do

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 question isn’t whether to customize, it’s

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 post explains how AgentCore agents in

Observability agents are fast. They query alerts, correlate logs with traces, and produce a root cause hypothesis in minutes. The part that still takes time is verification. You read the agent’s text summary, open your observability tools in a browser, navigate to the trace waterfall, check the service map to scope impact, and cross-reference what

Amazon Quick Desktop brings governed, AI-assisted reporting to the files your team already manages on Amazon FSx for NetApp ONTAP (FSx for ONTAP), cutting weekly report preparation from hours to minutes. Today, producing those reports takes hours of manual effort each week. Teams re-read the same documents, reformat metrics, and copy summaries into Slack. Leaders

Learn how to use Google Search tools to find home decor inspiration, shop for furniture, and tackle DIY projects. ​ 

Today, we are announcing new Ray capabilities on Amazon SageMaker HyperPod that integrate Ray with the HyperPod purpose-built infrastructure for foundation model training and serving. Ray is an open-source framework that data scientists use to scale distributed Python workloads across clusters of GPUs, from distributed training with Ray Train to model serving with Ray Serve.