Vellum provides the infrastructure enterprises need to easily build, evaluate and deploy reliable AI products at scale – now available for everyone to use This round — led by Leaders Fund, with participation from Socii Capital and existing investors Y Combinator, Rebel Fund and Eastlink Capital — enables Vellum to accelerate product innovation, expand into…
Developers are actively working to bring AI agents to market, but a significant hurdle has been the lack of memory. Without the ability to recall past interactions, agents treat each conversation as if it’s the first, leading to repetitive questions, an inability to remember user preferences, and a general lack of personalization. This results in…
This article explores what makes medical image annotation different from others and why it’s critical for building safe, effective AI systems in healthcare. What Is Medical Image Annotation? Medical image annotation is the process of adding labels to medical images, such as X-rays, ultrasounds, CT scans, MRI scans, and mammograms, to train machine learning models…
Phi-4-mini-Flash-Reasoning, the latest addition to Microsoft’s Phi-4 model family, is an open, lightweight language model designed to excel at long-context reasoning while maintaining high inference efficiency. Released on Hugging Face, this 3.8B parameter model is a distilled version of Phi-4-mini, fine-tuned for dense reasoning tasks like math problem solving and multi-hop question answering. Built using…
AI-powered video generation is improving at a breathtaking pace. In a short time, we’ve gone from blurry, incoherent clips to generated videos with stunning realism. Yet, for all this progress, a critical capability has been missing: control and Edits While generating a beautiful video is one thing, the ability to professionally and realistically edit it—to…
As AI models become increasingly sophisticated and specialized, the ability to quickly train and customize models can mean the difference between industry leadership and falling behind. That is why hundreds of thousands of customers use the fully managed infrastructure, tools, and workflows of Amazon SageMaker AI to scale and advance AI model development. Since launching…
Amazon SageMaker HyperPod now provides a comprehensive, out-of-the-box dashboard that delivers insights into foundation model (FM) development tasks and cluster resources. This unified observability solution automatically publishes key metrics to Amazon Managed Service for Prometheus and visualizes them in Amazon Managed Grafana dashboards, optimized specifically for FM development with deep coverage of hardware health, resource…
Amazon SageMaker now offers fully managed support for MLflow 3.0 that streamlines AI experimentation and accelerates your generative AI journey from idea to production. This release transforms managed MLflow from experiment tracking to providing end-to-end observability, reducing time-to-market for generative AI development. As customers across industries accelerate their generative AI development, they require capabilities to…
Today, we’re excited to announce that Amazon SageMaker HyperPod now supports deploying foundation models (FMs) from Amazon SageMaker JumpStart, as well as custom or fine-tuned models from Amazon S3 or Amazon FSx. With this launch, you can train, fine-tune, and deploy models on the same HyperPod compute resources, maximizing resource utilization across the entire model…
AI developers and machine learning (ML) engineers can now use the capabilities of Amazon SageMaker Studio directly from their local Visual Studio Code (VS Code). With this capability, you can use your customized local VS Code setup, including AI-assisted development tools, custom extensions, and debugging tools while accessing compute resources and your data in SageMaker…