\"This partnership will allow us to bring our advanced technology and innovative approach to an even wider audience, empowering developers and organizations around the world to harness the power of language AI and stay ahead of the curve in an increasingly competitive market.\"\n\nThe Cohere Medium generation language model available through SageMaker, provide developers with three key benefits:\n\nBuild, iterate, and deploy quickly – Cohere empowers any developer (no NLP, ML, or AI expertise required) to quickly get access to a pre-trained, state-of-the-art generation model that understands context and semantics at unprecedented levels. Our joint customers can now leverage the broad range of Amazon SageMaker services and integrate Cohere\'s model with their applications for accelerated time-to-value and faster innovation.\"\n\"As Cohere continues to push the boundaries of language AI, we are excited to join forces with Amazon SageMaker,\" said Saurabh Baji, Senior Vice President of Engineering at Cohere. \"We\'re excited to offer Cohere\'s general purpose large language model with Amazon SageMaker. The Medium model is deployed in containers that enable low-latency inference on a diverse set of hardware accelerators available on AWS, providing different cost and performance advantages for SageMaker customers.\n\n\"Amazon SageMaker provides the broadest and most comprehensive set of services that eliminate heavy lifting from each step of the machine learning process,\" said Rajneesh Singh, General Manager AI/ML at Amazon Web Services. The Medium generation model excels at tasks that require fast responses, such as question answering, copywriting, or paraphrasing. Companies can use the models out of the box or tailor them to their particular needs using their own custom data.\n\nDevelopers using SageMaker will have access to Cohere\'s Medium generation language model. The company builds and continually improves its general-purpose large language models (LLMs), making them accessible via a simple-to-use platform. Cohere helps developers and businesses automate a wide range of tasks, such as copywriting, named entity recognition, paraphrasing, text summarization, and classification. The company\'s mission is to enable developers and businesses to add language AI to their technology stack and build game-changing applications with it. Developers, data scientists, and business analysts use Amazon SageMaker to build, train, and deploy ML models quickly and easily using its fully managed infrastructure, tools, and workflows.\n\nAt Cohere, the focus is on language. This makes it easier for developers to deploy Cohere\'s pre-trained generation language model to Amazon SageMaker, an end-to-end machine learning (ML) service. Cohere\'s state-of-the-art language AI is now available through Amazon SageMaker. How to Write a Summary - Guide & Examples (from Scribbr.4 text= 'It\'s an exciting day for the development community. Summarize your thesis statement and the underlying meaning of the article.Īdapted from "Guidelines for Using In-Text Citations in a Summary (or Research Paper)" by Christine Bauer-Ramazani, 2020.Use transitional words and phrases to connect ideas.Start each paragraph with a topic sentence.Multi-paragraph summary - one paragraph per supporting detail, providing 2-3 examples for each.One-paragraph summary - one sentence per supporting detail, providing 1-2 examples for each. The number of paragraphs will depend on the length of the original article.Use the body paragraphs to explain the supporting ideas of your thesis statement.Provide a thesis statement that states the main idea of the article.Give an overview of the article, including the title and the name of the author.
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