AWS Machine Learning Blog Today, we’re excited to announce that the Gemma model is now available for customers using Amazon SageMaker JumpStart. Gemma is a family of language models based on Google’s Gemini models, trained on up to 6 trillion tokens of text. The Gemma family consists of two sizes: a 7 billion parameter model and a 2 […]Continue reading
AWS Machine Learning Blog Today, we are excited to announce that Code Llama foundation models, developed by Meta, are available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. Code Llama is a state-of-the-art large language model (LLM) capable of generating code and natural language about code from both code […]Continue reading
AWS Machine Learning Blog Today, we are excited to announce that the Mixtral-8x7B large language model (LLM), developed by Mistral AI, is available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. The Mixtral-8x7B LLM is a pre-trained sparse mixture of expert model, based on a 7-billion parameter backbone with […]Continue reading
AWS Machine Learning Blog Today we are excited to announce that the Llama Guard model is now available for customers using Amazon SageMaker JumpStart. Llama Guard provides input and output safeguards in large language model (LLM) deployment. It’s one of the components under Purple Llama, Meta’s initiative featuring open trust and safety tools and evaluations […]Continue reading
AWS Machine Learning Blog We are excited to announce the launch of Amazon DocumentDB (with MongoDB compatibility) integration with Amazon SageMaker Canvas, allowing Amazon DocumentDB customers to build and use generative AI and machine learning (ML) solutions without writing code. Amazon DocumentDB is a fully managed native JSON document database that makes it straightforward and […]Continue reading
AWS Machine Learning Blog Today, we are excited to announce support for Code Editor, a new integrated development environment (IDE) option in Amazon SageMaker Studio. Code Editor is based on Code-OSS, Visual Studio Code Open Source, and provides access to the familiar environment and tools of the popular IDE that machine learning (ML) developers know […]Continue reading
AWS Machine Learning Blog Today, we are excited to announce three launches that will help you enhance personalized customer experiences using Amazon Personalize and generative AI. Whether you’re looking for a managed solution or build your own, you can use these new capabilities to power your journey. Amazon Personalize is a fully managed machine learning […]Continue reading
AWS Machine Learning Blog Amazon Personalize is excited to announce the new Next Best Action (aws-next-best-action) recipe to help you determine the best actions to suggest to your individual users that will enable you to increase brand loyalty and conversion. Amazon Personalize is a fully managed machine learning (ML) service that makes it effortless for […]Continue reading
AWS Machine Learning Blog Today, we are excited to announce the capability to fine-tune the Mistral 7B model using Amazon SageMaker JumpStart. You can now fine-tune and deploy Mistral text generation models on SageMaker JumpStart using the Amazon SageMaker Studio UI with a few clicks or using the SageMaker Python SDK. Foundation models perform very […]Continue reading
AWS Machine Learning Blog We are excited to announce that Amazon SageMaker JumpStart can now stream large language model (LLM) inference responses. Token streaming allows you to see the model response output as it is being generated instead of waiting for LLMs to finish the response generation before it is made available for you to […]Continue reading
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