Merlin Yamssi

Oracle today announced the general availability of HeatWave #GenAI, including in-database LLMs, an automated in-database vector store, and the ability to have human-like contextual conversations in natural language. https://lnkd.in/gwZc7Pi3

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In 2013, Jay Kreps introduced the concept of the log as a fundamental abstraction for real-time data streaming, paving the way for platforms like #ApacheKafka. Over the past decade, the data streaming landscape has undergone a remarkable transformation, particularly with the rise of cloud-native environments and the integration of scalable object storage solutions like Amazon S3. This evolution has given rise to platforms like #ApachePulsar, each offering innovative approaches to log storage and data streaming. In our latest blog post, we explore the evolution of log storage implementations in data streaming and categorize this architectural journey into three distinct stages: 💡 Leader-Based Architecture Leader-based architectures form the foundation of most data streaming technologies used today. Traditional message queues rely on single-node brokers with primary-backup setups for high availability (HA). Kafka was the first distributed implementation, but it remains leader-based, where a designated leader manages replication. Pulsar advanced this by decoupling compute and storage, but it still follows the leader-based model. This approach works exceptionally well for scenarios requiring ultra-low latency (e.g., milliseconds). For such use cases, sending data through a leader remains unavoidable. 💡 Leaderless Architecture The emergence of S3 as a primary storage layer has triggered a shift toward leaderless architectures. Instead of treating S3 as just a tiered storage solution, it now serves as the main storage layer. In this model, brokers become leaderless, meaning any broker can handle reads and writes. Platforms like WarpStream, Confluent Freight Cluster, and StreamNative’s Ursa Engine are adopting this approach, reflecting the growing trend toward distributed and flexible architectures. 💡 Brokerless / Headless Architecture The next frontier is headless (or brokerless) architectures, where metadata and data are entirely decoupled: Data resides in S3, while metadata is managed by a separate metadata or catalog service. With a standard specification in place, operations no longer need to flow through brokers. Vendors can simply adhere to the standard and build their solutions accordingly. This approach can enable seamless scalability and interoperability. Our Ursa Engine is heading toward this exciting future. Explore the full blog post to dive deeper into these trends and the journey from leader-based to brokerless architectures: https://lnkd.in/gCcmv3rU You can also watch our keynote presentation at Data Streaming Summit: https://lnkd.in/g6a3wHrC

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One of my favorite quotes from Sundar Pichai during the Google - Alphabet earnings call this week: “Our commitment to innovation, as well as our long-term focus and investment in AI, are paying off and driving success for the company and for our customers.” Every day, I see first-hand the tangible business impact that Google Cloud's gen AI solutions can have for our customers – from increased revenue and reduced costs, to better customer engagement and employee satisfaction. Given our customers’ success in this space, it’s no surprise that our AI/ML capabilities were listed as our # 1️⃣ strength in the Critical Capabilities assessment as part of Gartner's 2024 Magic Quadrant™ for Strategic Cloud Platform Services. We made a notable jump in the Leader category this year – coming in ahead of AWS in Vision and ahead of MSFT in Execution. https://lnkd.in/gFhsG9vU

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🔥 The NVIDIA-AWS partnership is on fire! New assets are coming out almost every day! Thanks to the entire team for making this big effort! If you're working on deploying or optimizing large models with NVIDIA NIM and NeMo on AWS GPUs, follow me and send me the questions so we can help you keep up with the new stuff! Our Today's blog is Part 2 of a step-by-step guide on deploying Generative AI applications using NVIDIA NIM on Amazon EKS. In this post, we focus on creating a cluster with G5 instances accelerated by NVIDIA A10G GPUs and demonstrate how to use Cluster Auto Scaler + Horizontal Pod Auto Scaler for efficient, scalable AI model inference. Stay tuned for more powerful tools and insights to help you deploy and scale your AI models seamlessly! 🚀 #GenerativeAI #AI #NVIDIA #AWS #EKS #CloudComputing #AIInference #ScalingAI

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It was just yesterday that we saw GitHub announce a Copilot Workspace Tech Preview. Now Amazon comes out swinging with what looks like a pretty serious entry into the AI coding assistance space. Will be diving into this one in more depth in the coming days. #AWS #AIDev #GenAI

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In this podcast, Google Cloud's Oliver Parker vice president, global generative AI GTM, and Miku Jha, director, AI/ML and generative AI partner engineering, sit down with Bob Evans to talk about how businesses can safely and efficiently scale their generative AI efforts, focusing on high-value opportunities while leveraging Google Cloud’s open platform, extensive ecosystem, and advanced AI/ML capabilities to deliver meaningful results for clients. Scaling Generative AI The Big Themes: Prioritizing cost and impact: Businesses are focused on the dual priorities of cost efficiency and impactful results when implementing generative AI. With numerous potential applications for generative AI, organizations need to evaluate which projects will deliver the highest value. This approach involves collaborating with partners to identify the opportunities that offer the most substantial benefits. Open ecosystem advantage: Google Cloud’s emphasis on openness and flexibility is a key differentiator in the AI landscape. Its commitment to open systems and platforms allows for various models and technologies, including first-party and open-source offerings. This open ecosystem approach enables businesses to leverage various AI models and infrastructure options, enhancing scalability and adaptability. End-to-end offerings: Google Cloud offers a comprehensive AI stack that supports a wide range of applications, from foundational models to advanced infrastructure. This end-to-end tool facilitates the development and scaling of sophisticated AI applications. The integration of various layers, including model capabilities, hardware options, and platform services, enables partners to deliver efficient and effective AI offerings. The Big Quote: “Having great models is one thing, but having differentiated infrastructure and a platform — all those three things come together, and I think we are unique in that sense.” More from Google Cloud: Learn more about Google Cloud and AI.

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Graphs offer unparalleled potential for uncovering and leveraging intricate data relationships, driving deeper insights. However, the management of separate graph databases has historically hindered the realization of comprehensive, integrated analytics. Spanner Graph promises to revolutionize this landscape by seamlessly incorporating graph capabilities, thereby unlocking new avenues for data-driven value creation. #graphdatabases #googlecloud https://lnkd.in/dbkYMGYA

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Happy to share a set of announcements relating to Gemini and VertexAI. -- Model Updates: * New versions of Gemini 1.5 Pro and Flash with improved math, long context understanding, and vision. * Gemini 1.5 Flash is nearly 2.5x faster than GPT-4o mini. * Gemini 1.5 Pro's 2M context window is now generally available. -- Pricing: * Gemini 1.5 Pro pricing reduced by 50%. -- Imagen 3: * Generally available for Vertex AI customers on the allowlist. * Improved prompt understanding, instruction-following, photorealistic quality, and control over text within images. -- Open AI Ecosystem: * Support for new models from Meta, Mistral AI, and AI21 Labs. -- Vertex AI Platform Enhancements: * Controlled Generation for fine-grained control over AI outputs. * Batch API for faster developer workflows. * Supervised fine tuning for Gemini 1.5 Flash and Pro. * Distillation techniques for smaller, specialized models. * Prompt Optimizer for automatic prompt optimization. * Prompt Management SDK for versioning, restoring, and generating prompt suggestions. * Grounding with Google Search and dynamic retrieval. * Multimodal function calling. * Gen AI Evaluation Service for defining custom evaluation criteria. * Expanded data residency commitments.

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Amazon RDS Multi-AZ deployment with two readable standbys supports 6 additional AWS Regions Three-node Multi-AZ DB Cluster is now available in Asia Pacific (Hyderabad), Asia Pacific (Melbourne), Europe (Spain), Europe (Zurich), Middle East (UAE), and Israel (Tel Aviv) Regions. This deployment option supports minor version upgrades and system maintenance updates with typically less than one second of downtime when using Amazon RDS Proxy or any one of the open-source AWS Advanced JDBC Driver, PgBouncer, or ProxySQL. https://lnkd.in/g9D9RGVB #Amazon #RDS #PostgreSQL #MySQL

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🤓 No time to read the full white paper on NetApp's innovative RAG solution on docs.netapp.com, "Doc"? I've got you covered! This blog post breaks down the key insights so you can grasp how NetApp is revolutionizing AI and enhancing user experiences with technical content. Get a quick and informative overview without diving into the entire paper. Check out the blog post here: Read here: https://lnkd.in/epQBknnz #NetApp #ArtificialIntelligence #AIInnovation #RAGSystems #MachineLearning #UserExperience #TechnicalDocumentation #ContentExperience #Innovation

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Looking forward to meeting our customers in bayarea at the Google Sunnyvale office and discuss their vision on supply chain and showcase how AI can enable capabilities with step function improvement in KPIs. #pluto7 #GoogleCloud #sapongcp #sapbtp #sap #asug #uscsupplychain #gartnersc #genai #planninginabox #GoogleCloud #GCP

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This is a must see - 5mins of clear articulation of how and why Google Cloud differentiates in #GenAI with an unique approach and strategy relative to competitive #hyperscalers https://lnkd.in/gywMw7Dp

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Compute Engine offers a variety of ways to reduce costs, from optimizing your infrastructure to taking advantage of discounts. In this two-part blog post, I'll share some practical tips that can help you save money on Compute Engine. Whether you work for a small business just getting started with the cloud or a large enterprise looking to optimize its spending, there's something for everyone in this guide.

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Exploring Google Cloud networking enhancements for generative #AI applications #cloudcomputing #network #noc

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Interesting case study from NBA leveraging AI for data capture to enhance team and customer experiences. Discover more about this innovative approach here. #azure #azureai

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Do you need an Amazon EC2 instance with a truly stupendous amount of memory? Check out my newest blog post to learn about the new U7i high memory instances with 12 TiB to 32 TiB of DDR5 memory and 896 vCPUs, available today in four #AWS Regions. https://lnkd.in/gVgxJ-bF

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Hear from Sachin Gupta, VP and GM, Infrastructure and Solution Group, Google Cloud to learn customer-proven strategies for AI-powered innovation and modernization, leveraging the best of public cloud, edge, sovereign, and cross-cloud infrastructure and services. See how leaders are achieving real business outcomes faster and more cost-effectively in industries like retail, healthcare, and even the most stringent regulated industries. Plus, see how Gemini is dramatically simplifying and reimagining cloud as we know it.

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🚀 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱'𝘀 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: 𝗔 𝗡𝗲𝘄 𝗘𝗿𝗮 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 I am so thrilled at the launch of 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱'𝘀 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺, a transformative initiative designed to empower our partners in developing and deploying 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 that enhance customer experiences and streamline operations. The demand for AI agents is surging as businesses seek to automate complex tasks and personalize customer interactions. Projections indicate that by 2028, 𝟯𝟯% of enterprise software applications will incorporate agentic AI, a significant increase from less than 1% in 2024. This advancement is expected to enable 𝟭𝟱% of day-to-day work decisions to be made autonomously, underscoring the critical role AI agents will play in the future of business operations. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗦𝗽𝗮𝗰𝗲: We're also introducing 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗦𝗽𝗮𝗰𝗲, a new category in the Google Cloud Marketplace, enabling customers to easily discover and deploy partner-built AI agents tailored to their specific needs. The AI Agent Ecosystem provides our partners with the essential tools and support to develop and deliver value-driven AI solutions. It also empowers our customers to confidently select and swiftly deploy trusted, enterprise-ready AI agents built on the latest Google Cloud Product innovations, accelerating time to value and driving significant business impact. We are eager to expand our collaboration with our partners on this exciting journey to shape the future of AI-Agent driven business solutions! #Google #GCP #AI #AIAgents #VertexAI #LLM  #KnowledgeGraphs  #Enterprise #Evolution  #AI  #IntelligentOrganizations  #Google #GoogleCloud #AIAgents Alison Kenney Paul Aneesha Chhikara Yateendar Bollini Victor Morales Nenshad Bardoliwalla Jason Gelman Pramod Vasanth Rayn Veerubhotla Noel Kenehan Salim Hasham Rohit Sood Aman Azad Colleen Kapase Sabina Joseph Rodrigo Rocha Seth Siciliano Ritika Suri Abhi Kunté https://lnkd.in/gpHArCVD

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For those interested in how cloud scale database systems work, below is a deep dive on Amazon MemoryDB. MemoryDB delivers microsecond-level read latencies, and throughput in the hundreds of millions of queries per second, while offering multi-AZ durability with automatic failover.

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