Browsing: endpoints

Attackers increasingly target the packages, editor extensions, and AI tool configs on developer machines and not just production systems. Perplexity has open-sourced an internal tool it…

Today, Amazon SageMaker AI introduces OpenAI-compatible API support for real-time inference endpoints. If you use the OpenAI SDK, LangChain, or Strands Agents, you can now invoke…

As organizations scale generative AI workloads in production, securing reliable GPU compute has become one of the most persistent operational challenges. Large language models (LLMs) and…

Deploying large language models (LLMs) for inference requires reliable GPU capacity, especially during critical evaluation periods, limited-duration production testing, or burst workloads. Capacity constraints can delay…

Running machine learning (ML) models in production requires more than just infrastructure resilience and scaling efficiency. You need nearly continuous visibility into performance and resource utilization.…

Organizations increasingly deploy custom large language models (LLMs) on Amazon SageMaker AI real-time endpoints using their preferred serving frameworks—such as SGLang, vLLM, or TorchServe—to help gain…

This post is cowritten with Aashraya Sachdeva from Observe.ai. You can use Amazon SageMaker to build, train and deploy machine learning (ML) models, including large language…