Building an end to end Generative AI stack on Oracle Cloud Infrastructure (OCI) requires a multi-layered approach for integrating artificial intelligence within enterprise systems. This reference architecture describes a four layer AI stack and all the different components that are needed to implement an enterprise grade Generative AI solution within an enterprise setting. 1. Application Layer 2. Access Layer 3. Logging and Monitoring across the solution 4. AI layer consisting of the following five modules: - AI integration - LLM - AI Development - Data Integration - Context and Data Catalog For Strategy and Finance you can consider the following use cases: a. Synthesis of unstructured data from earnings calls, analyst reports and other sources. b. Automation of complex, context heavy processes like expensing. c. At scale monitoring of competitors and customers, across public or private sources More information on this link: https://lnkd.in/gH3qHUch David Ricardo Orellana Moyao Ernesto Osorno Rodriguez Monica Salaris Luis Valdez Cassius Di Cianni Thiago G.O. do Amaral Vitor Alves Junior Diego Chaves Carlos Chacon Tosso Marcos E Palma M Acknowledgments Author: Badr Tharwat
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An interesting Gen AI solution reference architecture that uses OCI components that applies to different use cases (Finance, Sales, Marketing, Product Development) . . . #oci #generativeai #oraclecloudinfrastructure
Building an end to end Generative AI stack on Oracle Cloud Infrastructure (OCI) requires a multi-layered approach for integrating artificial intelligence within enterprise systems. This reference architecture describes a four layer AI stack and all the different components that are needed to implement an enterprise grade Generative AI solution within an enterprise setting. 1. Application Layer 2. Access Layer 3. Logging and Monitoring across the solution 4. AI layer consisting of the following five modules: - AI integration - LLM - AI Development - Data Integration - Context and Data Catalog For Strategy and Finance you can consider the following use cases: a. Synthesis of unstructured data from earnings calls, analyst reports and other sources. b. Automation of complex, context heavy processes like expensing. c. At scale monitoring of competitors and customers, across public or private sources More information on this link: https://lnkd.in/gH3qHUch David Ricardo Orellana Moyao Ernesto Osorno Rodriguez Monica Salaris Luis Valdez Cassius Di Cianni Thiago G.O. do Amaral Vitor Alves Junior Diego Chaves Carlos Chacon Tosso Marcos E Palma M Acknowledgments Author: Badr Tharwat
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The #oracle #CloudWorld is over. There were so many exciting announcements For example this one: Organizations benefit from a fully managed RAG service with support for Oracle Database 23ai AI Vector Search without manual integration #OCI Generative AI now provides access to Meta’s Llama 3.1 models in addition to Cohere Command R and Command R+ models
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This is officially the beginning of Oracle's GenAI journey. #OCI Generative AI Service is now Live with initial #choice of #LLMs from Cohere (XL and Light) and Meta (#Llama2 -270B) in the #PublicCloud and on-premises, in Dedicated Regions. The service now includes support for 100+ languages, secure/dedicated #cluster management, and flexible #finetuning options aiming at #enterprise use. Our customers can further #refine the #aimodels using their own #data with #retrievalaugmentedgeneration (RAG) techniques, so the models will understand their unique internal operations. Now in #beta, the Generative AI Agents Service with a RAG agent combines the power of LLMs and #enterprisesearch built on OCI #OpenSearch Service to provide #contextual results that are enhanced with customer data. Stay tuned, more to come very soon 😎 More on the GenerativeAI Service release 👉 https://lnkd.in/d_Sb2-Y6 More on the RAG-Agents Service beta 👉 https://lnkd.in/dAsvAeWM #oracle #oraclecloudinfrastructure #oraclecloud #generativeai #genai #aiagents #secureai #cohere #llama2 #dataai
Oracle Embeds Generative AI Across the Technology Stack to Enable Enterprise AI Adoption at Scale
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Oracle Analytics 2024 - Install Base Modernization In today’s highly competitive business environment, every organization must be concerned with extracting meaningful insights from data to make more informed decisions. They must improve efficiency, productivity, streamline operations, reduce costs, understand customer needs, increase sales and maximize profits. Oracle Analytics has been recognized by industry experts for innovations and use of technologies, such as AI, machine learning, and natural language processing. Current installed base customers who upgrade to either OAC or OAS can access ready-to-use services across their business analytics workflow for connecting, preparing, modeling, exploring, sharing, and consuming insights from all their data. Learn about how current Oracle Analytics installed base customers can upgrade to Oracle Analytics Cloud or Oracle Analytics Server. #oracle #oci #cloud #analytics
Oracle Analytics 2024 - Install Base Modernization | Learn Oracle | Oracle Partner Enablement Revenue Services
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I know what you are thinking, not another AI post! However, if you are an Oracle Partner (especially an Oracle Construction & Engineering ecosystem partner!), then I urge you to take a look at this announcement. Creating capability through generative AI utilising Cohere or Meta's models on OCI right next to Oracle's Smart Construction Platform suite of applications also on OCI, should set the imagination going on what you can deliver to our collective customers. The team and I would love to discuss the opportunity this provides for new and exciting secure solutions provided by our partners in the engineering and construction industry. As Ritu Jyoti is quoted as saying: "With a common architecture for generative AI that is being integrated across the Oracle ecosystem ... Oracle is bringing generative AI to where exabytes of customer data already reside, both in cloud data centers and on-premises environments. This greatly simplifies the process for organizations to deploy generative AI with their existing business operations."
Oracle Embeds Generative AI Across the Technology Stack to Enable Enterprise AI Adoption at Scale
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𝐕𝐄𝐋𝐎: 𝐀 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞-𝐀𝐬𝐬𝐢𝐬𝐭𝐞𝐝 𝐂𝐥𝐨𝐮𝐝-𝐄𝐝𝐠𝐞 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐯𝐞 𝐋𝐋𝐌 𝐐𝐨𝐒 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 📘 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐩𝐚𝐩𝐞𝐫 𝐚𝐛𝐨𝐮𝐭? This paper presents VELO, an innovative framework designed to optimize the quality of service (QoS) for large language models (LLMs) through a collaborative approach between cloud and edge computing. By leveraging vector databases, VELO enhances the performance, scalability, and efficiency of LLM deployments. 🤖 𝐊𝐞𝐲 𝐂𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬: 𝐏𝐨𝐢𝐧𝐭 1: Introduction of VELO, a framework that combines cloud and edge computing to optimize LLM performance. 𝐏𝐨𝐢𝐧𝐭 2: Utilization of vector databases to manage and query large-scale data efficiently. 𝐏𝐨𝐢𝐧𝐭 3: Demonstration of significant improvements in QoS for LLM applications across various scenarios. 🚀 𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡? 𝐏𝐨𝐢𝐧𝐭 1: Addresses the scalability challenges faced by LLMs in real-world applications. 𝐏𝐨𝐢𝐧𝐭 2: Enhances the efficiency and speed of LLM deployments by distributing workloads between cloud and edge environments. 𝐏𝐨𝐢𝐧𝐭 3: Provides a robust solution for managing large datasets, leading to improved performance and user experience. 🔬 𝐊𝐞𝐲 𝐅𝐢𝐧𝐝𝐢𝐧𝐠𝐬: 𝐏𝐨𝐢𝐧𝐭 1: VELO significantly reduces latency and improves response times for LLM queries. 𝐏𝐨𝐢𝐧𝐭 2: The framework effectively balances the computational load between cloud and edge resources. 𝐏𝐨𝐢𝐧𝐭 3: Demonstrates higher throughput and better resource utilization compared to traditional LLM deployment methods. 🔧 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞: 𝐏𝐨𝐢𝐧𝐭 1: Potential to revolutionize the deployment and scalability of LLMs in various industries. 𝐏𝐨𝐢𝐧𝐭 2: Encourages further research into cloud-edge collaborative frameworks for other AI applications. 𝐏𝐨𝐢𝐧𝐭 3: Promotes the development of more efficient and scalable AI solutions leveraging vector databases. 💡 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: 𝐏𝐨𝐢𝐧𝐭 1: VELO offers a cutting-edge solution for optimizing the performance of LLMs. 𝐏𝐨𝐢𝐧𝐭 2: The framework's collaborative approach between cloud and edge computing is highly effective. 𝐏𝐨𝐢𝐧𝐭 3: Vector databases play a crucial role in managing and querying large-scale data efficiently. VELO introduces a transformative approach to optimizing large language models through a collaborative cloud-edge framework, leveraging the power of vector databases to deliver superior performance and scalability
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Use Case # 530: Oracle’s HeatWave on Amazon Web Services (AWS) now provides in-database GenAI solutions, enabling businesses to build AI apps without moving data. By consolidating multiple AI services into a single system, HeatWave helps reduce costs and simplifies AI adoption for businesses. Key features include multi-language support, AutoML, and real-time data updates, making it a versatile choice for companies looking to leverage AI easily and securely. Learn more: https://lnkd.in/gmf2RbJN #Oracle #HeatWave #GenAI #AWS #AI #CloudInfrastructure
Oracle HeatWave Unveils Enhancements with GenAI Integration
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We're making it easier to integrate #AI into modern workloads. Learn how we've embedded #generativeAI across the technology stack to enable enterprise #AI adoption at scale: https://lnkd.in/eRa76EZ8.
Oracle Embeds Generative AI Across the Technology Stack to Enable Enterprise AI Adoption at Scale
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IBM watsonx.data beats Databricks performance at 60% lower costs. Delivering superior price performance and enhanced data management for AI with watsonx.data #IBM # watsonx
This is how IBM rocks!! #Think2024 - an event to remember, highlighting that AI is undoubtedly the future. We are also proud to announce that we delivered superior price-performance and enhanced data management for AI with IBM Watsonx.data. IBM watsonx.data with Presto C++ v0.286 and query optimizer on IBM Storage Fusion HCI, tested internally by IBM, was able to deliver better price performance compared to Databrick’s Photon engine, with equal query runtime at less than 60% of the cost, derived from public 100 TB TPC-DS Query benchmarks. Many teams across IBM Data & AI Organization had to come together to make this happen, and special thanks go to each and every one who stayed with us throughout the journey. Thanks to the leadership team for believing in and supporting this mission: Dinesh Nirmal Steven Astorino Vikram Murali Remus Lazar Denis Kennelly Melissa Modjeski Vincent Hsu Sriram Raghavan Vincenzo Pasquantonio Sripriya Srinivasan Hamid Pirahesh Abdel Labbi Steven Mih Shweta Shandilya Thanks to the Key technical teams for sacrificing several weekends, holidays, and nights to make this happen: Ashok Kumar Yiqun (Ethan) Zhang Christian Zentgraf Aditi Pandit Deepak Majeti Ying Su Karteek Murthy Pramod Sathyanarayana Minhan Cao Sujit Madiraju Zoltan Arnold Nagy Michael Kaufmann Gregory Kishi Khanh Ngo Berthold Reinwald Calisto Zuzarte Ajay Gupta Pascal Spörri Michael Ohsaka Olga Yiparaki Gopikrishnan Varadarajulu Farhana Haider Olivier Bernin Richard Sidle George Lapis Ioana Delaney Anant Aneja Arin Mathew Austin Clifford Shrinivas Kulkarni Jason Sizto Kanchana Padmanabhan Sandhya Srikumar Special thanks to our product management team who supported us all along: Edward Calvesbert Joshua Kim Sonia M. Minaz Merali Yuankai (Kevin) Shen Hans Uhlig Fariya Syed-Ali (Product Marketing Lead) Thanks to our partners - Murali Madhanagopal Irena Rogovsky Ryan Park https://lnkd.in/gzMgnvnH
Delivering superior price-performance and enhanced data management for AI with IBM watsonx.data - IBM Blog
https://www.ibm.com/blog
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Solve your data challenges with ease! IBM offers a powerful software-defined storage platform that is specifically designed for AI, machine learning, and high-performance computing workloads. This scale-out file and object storage solution is perfect for organizations looking to leverage cutting-edge technology to improve their data management capabilities. Check out the link below to learn more! https://lnkd.in/gZmdbm_V #AI #MachineLearning #HighPerformanceComputing #DataManagement #StorageSolutions #ibmstorage #storage4AI #nvdia #ai
Target Use Cases for Parllel File Systems Extend to Enterprise AI and Analytics
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