![]() The program is part of AWS’ managed quantum computing service, named Amazon Braket, which was introduced in 2020.Īmazon Bracket Direct allows researchers across enterprises to get private access to the full capacity of various quantum processing units (QPUs) without any wait time and also provides the option to receive expert guidance for their workloads from AWS’ team of quantum computing specialists, AWS said.Ĭurrently, the Direct program supports the reservation of IonQ Aria, QuEra Aquila, and Rigetti Aspen-M-3 quantum computers. The cloud services provider has announced a new program, dubbed Amazon Braket Direct, to offer researchers direct, private access to quantum computers. Amazon Braket for reserving quantum computers ![]() Last Tuesday, AWS CEO Adam Selipsky premiered the star of the cloud giant's re:Invent 2023 conference: Amazon Q, the company's answer to Microsoft’s GPT-driven Copilot generative AI assistant.Īmazon Q can be used by enterprises across a variety of functions including developing applications , transforming code, generating business intelligence, acting as a generative AI assistant for business applications, and helping customer service agents via the Amazon Connect offering. Amazon Q - the generative AI assistant for everything SageMaker also now features the Model Evaluation capability, now called SageMaker Clarify, which can be accessed from within the SageMaker Studio. The no code platform supports LLMs from Anthropic, Cohere, and AI21 Labs. In order to do so, Inference allows enterprises to deploy multiple models to the same cloud instance to better utilize the underlying accelerators.ĪWS has also updated its low code machine learning platform targeted at business analysts, SageMaker Canvas.Īnalysts can use natural language to prepare data inside Canvas in order to generate machine learning models, said Swami Sivasubramanian, head of database, analytics and machine learning services for AWS. ![]() SageMaker Inference, on the other hand, is targeted at helping enterprise reduce model deployment cost and decrease latency in model responses. ![]() In contrast to the manual model training process - which is prone to delays, unnecessary expenditure and other complications - HyperPod removes the heavy lifting involved in building and optimizing machine learning infrastructure for training models, reducing training time by up to 40%, the company said. In order to help enterprises train and deploy large language models efficiently, AWS introduced two new offerings - SageMaker HyperPod and SageMaker Inference - within its Amazon SageMaker AI and machine learning service. Updates to Amazon SageMaker for supporting generative AI In addition, the cloud services provider has added a model in preview, Amazon Titan Image Generator, to the AI app-building service.ĪWS also has released a new feature within Bedrock that allows enterprises to evaluate, compare, and select the best foundational model for their use case and business needs.ĭubbed Model Evaluation on Amazon Bedrock and currently in preview, the feature is aimed at simplifying several tasks such as identifying benchmarks, setting up evaluation tools, and running assessments, the company said, adding that this saves time and cost. Amazon also has added its proprietary Titan Text Lite and Titan Text Express foundation models to Bedrock. Updated models added to Bedrock include Anthropic’s Claude 2.1 and Meta Llama 2 70B, both of which have been made generally available. NeMo Retriever is a generative AI microservice that enables enterprises to connect custom large language models (LLMs) to enterprise data, so the company can generate proper AI responses based on their own data.įurther, AWS said that it will be the first cloud provider to bring Nvidia's GH200 Grace Hopper Superchips to the cloud. Nvidia also shared plans to integrate its NeMo Retriever microservice into AWS to help users with the development of generative AI tools like chatbots. Trainium2, on the other hand, is designed to deliver up to four times faster training than first-generation Trainium chips.Īt re:Invent, AWS also extended its partnership with Nvidia, including support for the DGX Cloud, a new GPU project named Ceiba, and new instances for supporting generative AI workloads. The Graviton4 processor, according to AWS, provides up to 30% better compute performance, 50% more cores, and 75% more memory bandwidth than the current generation Graviton3 processors.
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