We're thrilled to announce the integration of Amazon Web Services (AWS) product Bedrock into our data collection operations, enhancing the capabilities of our latest product, ApartmentIQ. ApartmentIQ automates the manual market survey process using 100% public data, providing property management companies with accurate, daily, and comprehensive data. Find out more about our collaboration below!
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In this blog post, you will see how Amazon Bedrock Agents (in preview) extend FMs to run complex business tasks, all without writing any code. With fully managed agents, you don’t have to worry about provisioning or managing infrastructure. Take a look at it #aws #awscloud #machinelearning #genai #amazonbedrock https://lnkd.in/dY6V8GA8
Build a foundation model (FM) powered customer service bot with agents for Amazon Bedrock | Amazon Web Services
aws.amazon.com
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Start monitoring 𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 with Datadog today! We’re pleased to announce that Datadog integrates with 𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸, allowing you to monitor your FM usage, API performance, and error rate with runtime metrics and logs. In this post, we’ll cover how you can 𝗺𝗼𝗻𝗶𝘁𝗼𝗿 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀’ 𝗔𝗣𝗜 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲, 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝘁𝗵𝗲𝗶𝗿 𝘂𝘀𝗮𝗴𝗲, 𝗢𝗢𝗧𝗕 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 and our 𝗔𝗜/𝗠𝗟 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 with 𝗕𝗶𝘁𝘀 𝗔𝗜. Read all about it here! #Datadog #AWS #AWSBedrock #GenAI #AIModels #LLM #AIML #ML #BitsAI
Monitor Amazon Bedrock with Datadog
datadoghq.com
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Tldr; #AWS launched several features at re: invent! That definitely makes a blog post worth it, right? 🚀 Releasing a blog on making it fun to understand agents @ bedrock and Knowledge bases for a specific use case. Feel free to take a read and reach out with any questions! It is an extension of my previous product blog! Link: https://lnkd.in/gDpRn5ay #aws #agents #bedrock
Scaling Bedrock Agents to Fully Orchestrate & Augment User Workflows
medium.com
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Dream RAG LLM: - Good size for deploying in one A100 (35B params) - Multilingual - Tool use - Fine-tuned for grounding answers on the documents provided - Two citations modes for trade-offs in speed-quality - I guess that it must be good at refusing out of domain questions
Introducing Command-R, our new RAG-optimized LLM aimed at large-scale production workloads. Command-R fits into the emerging “scalable” category of models that balance high efficiency with strong accuracy, enabling companies to move beyond proof of concept, and into production. Command-R is a generative model optimized for long context tasks such as retrieval augmented generation (RAG) and using external APIs and tools. It’s designed to work in concert with Cohere’s industry-leading Embed and Rerank models to provide best-in-class integration for RAG applications and excel at enterprise use cases — across the 10 major languages of the global market. Command-R will be available immediately on Cohere’s hosted API, and on major cloud providers in the near future. In keeping with Cohere’s core principles, it maintains a focus on privacy and data security. To learn more, visit our blog: https://lnkd.in/eUbSTyRr
Command R: RAG at Production Scale
txt.cohere.com
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Reinvent 2023: A Landmark for AI Innovation, but Economic Challenges Remain - AWS Introduces Cost Optimisation Hub to guide companies through 2024 turbulent financial waters and enhance return of investment. #aws #finops
New Cost Optimization Hub centralizes recommended actions to save you money | Amazon Web Services
aws.amazon.com
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𝐂𝐨𝐡𝐞𝐫𝐞 𝐂𝐨𝐦𝐦𝐚𝐧𝐝-𝐑 A model specifically tuned for RAG work-loads in production. 🧰 Optimized for RAG & tool-use 🧵 Long context (128k) 🔢 35B parameters - Can work on single GPU 💾 Open-weights 🇺🇳 10 languages 💪 Strong RAG results
Introducing Command-R, our new RAG-optimized LLM aimed at large-scale production workloads. Command-R fits into the emerging “scalable” category of models that balance high efficiency with strong accuracy, enabling companies to move beyond proof of concept, and into production. Command-R is a generative model optimized for long context tasks such as retrieval augmented generation (RAG) and using external APIs and tools. It’s designed to work in concert with Cohere’s industry-leading Embed and Rerank models to provide best-in-class integration for RAG applications and excel at enterprise use cases — across the 10 major languages of the global market. Command-R will be available immediately on Cohere’s hosted API, and on major cloud providers in the near future. In keeping with Cohere’s core principles, it maintains a focus on privacy and data security. To learn more, visit our blog: https://lnkd.in/eUbSTyRr
Command R: RAG at Production Scale
txt.cohere.com
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AWS Cloud Infrastructure Optimizing Customer Engagement Profitability & Productivity Leveraging Generative AI Machine Learning | Business Performance Optimization | Data Protection | Security | Stakeholder Relationships
Amazon Web Services (AWS) has just announced the general availability of #AmazonQ Business, a secure, private, generative AI assistant that empowers your organization’s users to be more creative, data-driven, efficient, prepared, and productive. During the preview, Amazon received lots of customer feedback and used that feedback to prioritize their enhancements to the service. One of the most exciting features of Amazon Q Business is its seamless integration with over 40 popular enterprise data sources, including Amazon Simple Storage Service (Amazon S3), Microsoft 365, and Salesforce. It ensures that you access content securely with existing credentials using single sign-on, according to your permissions, and also includes enterprise-level access controls. Looking to increase your organization's speed to market and problem-solving capabilities? Check out Amazon Q Business and see how it can help you achieve your goals! https://lnkd.in/gA-RtVJe
AWS announces general availability of Amazon Q, generative AI-powered assistant
aboutamazon.com
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In this post, we show how you can automate and intelligently process derivative confirms at scale using AWS AI services. The solution combines Amazon Textract, a fully managed ML service to effortlessly extract text, handwriting, and data from scanned documents, and AWS Serverless technologies, a suite of fully managed event-driven services for running code, managing data, and integrating applications, all without managing servers.
Automate derivative confirms processing using AWS AI services for the capital markets industry | Amazon Web Services
aws.amazon.com
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At AWS, securing AI infrastructure refers to zero access to sensitive AI data, such as AI model weights and data processed with those models, by any unauthorized person, either at the infrastructure operator or at the customer. It’s comprised of three key principles: Complete isolation of the AI data from the infrastructure operator – The infrastructure operator must have no ability to access customer content and AI data, such as AI model weights and data processed with models. Ability for customers to isolate AI data from themselves – The infrastructure must provide a mechanism to allow model weights and data to be loaded into hardware, while remaining isolated and inaccessible from customers’ own users and software. Protected infrastructure communications – The communication between devices in the ML accelerator infrastructure must be protected. All externally accessible links between the devices must be encrypted.
A secure approach to generative AI with AWS | Amazon Web Services
aws.amazon.com
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Leveraging AWS Q is a essential for 2024, it’s business utility for client’s are indeed vast and valuable. Get your AWS Business insights sooner with Q! #awscloud
Introducing Amazon Q, a new generative AI-powered assistant (preview) | Amazon Web Services
aws.amazon.com
