This blog post is co-written with Moran beladev, Manos Stergiadis, and Ilya Gusev from Booking.com. Large language models (LLMs) have revolutionized the field of natural language processing with their ability to understand and generate humanlike text. Trained on broad, generic datasets spanning a wide range of topics and domains, LLMsContinue Reading
This is a guest post co-written with Tim Krause, Lead MLOps Architect at CONXAI. CONXAI Technology GmbH is pioneering the development of an advanced AI platform for the Architecture, Engineering, and Construction (AEC) industry. Our platform uses advanced AI to empower construction domain experts to create complex use cases efficiently.Continue Reading
In production generative AI applications, responsiveness is just as important as the intelligence behind the model. Whether it’s customer service teams handling time-sensitive inquiries or developers needing instant code suggestions, every second of delay, known as latency, can have a significant impact. As businesses increasingly use large language models (LLMs)Continue Reading
Amazon SageMaker provides a seamless experience for building, training, and deploying machine learning (ML) models at scale. Although SageMaker offers a wide range of built-in algorithms and pre-trained models through Amazon SageMaker JumpStart, there are scenarios where you might need to bring your own custom model or use specific softwareContinue Reading
In this post, I’ll show you how to use Amazon Bedrock—with its fully managed, on-demand API—with your Amazon SageMaker trained or fine-tuned model. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta,Continue Reading
Today, Amazon SageMaker is excited to announce updates to the inference optimization toolkit, providing new functionality and enhancements to help you optimize generative AI models even faster. These updates build on the capabilities introduced in the original launch of the inference optimization toolkit (to learn more, see Achieve up toContinue Reading
In Part 1 of this series, we introduced Amazon SageMaker Fast Model Loader, a new capability in Amazon SageMaker that significantly reduces the time required to deploy and scale large language models (LLMs) for inference. We discussed how this innovation addresses one of the major bottlenecks in LLM deployment: the timeContinue Reading
The generative AI landscape has been rapidly evolving, with large language models (LLMs) at the forefront of this transformation. These models have grown exponentially in size and complexity, with some now containing hundreds of billions of parameters and requiring hundreds of gigabytes of memory. As LLMs continue to expand, AIContinue Reading
Today at AWS re:Invent 2024, we are excited to announce the new Container Caching capability in Amazon SageMaker, which significantly reduces the time required to scale generative AI models for inference. This innovation allows you to scale your models faster, observing up to 56% reduction in latency when scaling aContinue Reading
Today at AWS re:Invent 2024, we are excited to announce a new feature for Amazon SageMaker inference endpoints: the ability to scale SageMaker inference endpoints to zero instances. This long-awaited capability is a game changer for our customers using the power of AI and machine learning (ML) inference in theContinue Reading
