Srini Iragavarapu

In collaboration with AWS, we are thrilled to announce the Confluent blueprint for Amazon OpenSearch Ingestion! This serverless feature enables customers to effortlessly transfer data from Confluent to OpenSearch Domains or OpenSearch Serverless collections. This capability unlocks numerous new use cases, such as Retrieval-Augmented Generation (RAG), real-time log analytics, and more. Read the full announcement, including a complete example of setting up a Confluent/OpenSearch pipeline using the new blueprint (with optional use of Schema Registry), here ➡️

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Business leaders are looking to Microsoft to understand how #Copilot and agents will transform work—and we want to help. As an engineering leader who supports our sales organization, I’m looking to drive more revenue, so we’re leveraging AI and Copilot across our teams to help increase the pipeline and deal velocity, improve conversion rates, and ultimately improve the seller experience. But that’s just the beginning. We see Agents as being game changers for the sales function. We will have autonomous agents prospect and qualify leads at scale on behalf of sellers and help make them more productive and effective. Discover on #WorkLab how AI can future-proof your organization and keep you ahead of the curve. #AI #Sales #Innovation #Microsoft #FutureOfWork https://lnkd.in/gmJUbn2N

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Week 3 reflections at Oracle - Cloud regions and zones go way beyond just an availability feature. Today was the first time I saw Jae Evans live, and it was inspiring to hear her thoughts. I found it fascinating to get a glimpse of how Oracle viewed datacenter regions, how different it was to Amazon's perspective, and how culturally backed both these viewpoints were. During my time at Amazon, cloud regions were perceived fundamentally as an availability feature. A mechanism for customers to create redundancy in their architecture. A way for customers to fail-over to a functioning region if one becomes non-functional. A means to deliver functionality as close to end-customers as possible. This approach worked harmoniously for AWS's humble beginnings supporting mom-&-pop customers that wanted a space on the web for their local business, and fueling organic growth in supporting mid-sized businesses. But by the time large-enterprise and mission critical domains came about, AWS had to 'wedge-in' more nuanced customer requirements into the cloud architecture it had built over time. And it was painful. While Oracle shares similar tenets around availability, it's region strategy goes way beyond operations. Remember that Oracle was an established entity in the enterprise market way before the cloud era. When Oracle entered the cloud business, their customers expected the cloud to work for their needs from day-one. So Oracle innovated its way to offer four types of cloud offerings. It started out with the 'Public cloud' which is the most common style of cloud similar to AWS. But quickly pivoted to 'Hybrid cloud' when it learned its customers preferred to retain select workloads on their premises. It then pioneered 'Dedicated cloud' for customers that wanted a walled environment in the data center of their choice. And finally 'Multicloud' - a reflection of Oracle simultaneously viewing competitors as collaborators. Oracle Database@Azure for instance runs Oracle Cloud databases in Microsoft data centers. Oracle MySQL HeatWave on AWS is a fully managed Oracle Cloud database running on AWS and compatible Amazon Ads, Amazon Aurora and several more Amazon features. The debate on cloud leaders will continue on, but one thing is clear. Customers are the winners. And successful cloud providers not only align culture with their products, but also redefine it as the market evolves. Interested in our mission? Drop me a note! We are hiring! Sr. Principal - https://lnkd.in/gAgQS-9f Principal - https://lnkd.in/gY_u8rNw Sr. SDE - https://lnkd.in/gJgw4b94 SDE - https://lnkd.in/gr3VeQSd #Oracle #OCI #AWS #Cloud #Availability #Features #Hybrid

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Now CloudWatch cross-account enables customers to share their metrics across multiple source accounts with a central monitoring account. With cross-account anomaly detection, service owners who deploy applications across multiple accounts can now track resource performance and alarm on anomalous behavior all from a single location within their unified monitoring account. #aws #awscommunity #cloudwatch #observability https://lnkd.in/gr3Sp6-h

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RDS MySQL zero-ETL integration with Amazon Redshift is now generally available. #zeroetl #rds #redshift

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IndiaAI recently sat down with New Relic Chief Product Officer, Manav Khurana to chat about how AI is shaping the future of observability. Check out the full article below!

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The pace of innovation on Amazon Bedrock is accelerating. Amazon Bedrock announces new capabilities to empower customers to build secure and scalable generative AI applications. Tens of thousands of customers across industries are using Bedrock. Key announcements include: 1/ Custom Model Import to easily bring customized models to Bedrock 2/ Model Evaluation to quickly assess and select best models 3/ Guardrails for implementing safeguards against harmful content 4/ Expanding model choice with new Amazon Titan models, Meta, and Cohere https://lnkd.in/gTaUrpC4 #awscloud #amazonbedrock #generativeai

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Exciting New Capabilities for Amazon Bedrock! 🚀 Amazon Bedrock is growing rapidly, with tens of thousands of customers across industries choosing it as their generative AI foundation. Key updates: ✨ Custom Model Import lets you easily bring your own proprietary models to Bedrock and leverage its powerful capabilities. ✨ Model Evaluation helps you quickly analyze and compare models to find the best fit for your use case. ✨ Guardrails provides best-in-class technology to implement safeguards aligned with your responsible AI policies. ✨ Plus, new models like Amazon Titan Text Embeddings V2 for efficient retrieval augmented generation, and latest models from Cohere (Command R) and Meta (Llama 3) Exciting time for generative AI innovation on AWS! Also, not mentioned here but equally important: plenty of silicon to choose from to run those models: from new Nvidia chips to Amazon's own 2nd gen high perfomance low cost Inferentia and Trainium chips!! (Oh, and it's great to see our Travel and Hospitality customers mentioned in this PR: Choice Hotels International, Delta Air Lines, United Airlines, TUI ) Check out the details at: https://lnkd.in/gJHGQRYV Patricia Yates Jason Bennett Troy Barker Vasile Balan Davor Golac Steven M. Elinson Greg Land Clare Ward Massimo Morin Yogesh Bendre Abhijeet Patil Keith Papsin Ravi Kumar

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Will be really interesting to check how mixed Agentforce agents for Sales, Service, Marketing, Commerce, etc. work together that would balance the utilization of cloud computing resources, or instead influence cloud providers’ computing, memory, networking resource configurations.

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This insightful blog post by Manpreet(Manny) Sidhu explores how to leverage this low-power, long-range network with AWS IoT Core to build track and trace applications. Perfect for various asset tracking usecases!.

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Here’s how Amazon’s homegrown chips are helping customers power complex AI and general compute workloads to process enormous amounts of data at a lower cost.

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Discover how Oracle Cloud's computer vision and data science services improve the speed of #AI predictions by utilizing the NVIDIA Triton Inference Server: https://lnkd.in/dDwTSCQy

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Thats the exact explanation I gave to a group of investors last week. (… it’s just Precision/Recall, attributes vectorization as “embedding”, K-Nearest Neighbor, User Search Intent, DAG, ML algorithms, etc all over again …). LOL and they asked what’s new this time around. And I was like not much, ML is still the backbone, and AI is the inference and actions based on the model. It works better when there is a feedback loop in a closed system. But the current state of the art like ChatGPT is based on scraping the Internet (garbage in, garbage out). Kinda like early search engines. What is a better analogy is that NVIDIA/Snowflake etc are selling tools, aka pickaxes to prospectors. The gold is up to you to find. And the opportunity right now is to monetize industry specific (healthcare, travel, financial services) clean data to build models with (aka a treasure map for the prospectors). From an enterprise perspective, what’s out there today aka generative AI isn’t very useful. Too much hype. To build useful engines will require an effort similar to search revolution 2 decades ago. From

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More than 60% of all funded generative AI startups, and nearly 90% of today’s generative AI unicorns are Google Cloud customers. At #GoogleCloudNext we announced how we're making it easier for them to run on Google Cloud. Learn more ⬇️

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It’s really worth reading if you want to write better code.

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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/gqZ2ebDS

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New Episode: 260: Amazon Dispatches AWS CEO Adam Selipsky with Prime 2-day delivery Episode 260 of The Cloud Pod podcast, bringing you all the latest news from Cloud and AI including AWS, Azure, Google, OCI, GPT-4.o, C7i-flex, AWS CEO Changes and Amazon Polly #aws #gcp #azure #cloud #cloudpodcast #podcast

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Can you believe it's already been two weeks since the the first-ever #KafkaSummit Bangalore? ICYMI we have the full rundown of all the key moments from the premiere event for the #ApacheKafka community. We’ve got: 🔑 Keynote recaps (Big product announcements!) 💡 Session highlights (Flink, GenAI, and of course Kafka are top of mind) 🚀 Women in Technology spotlight ➕ And MORE Find it on our blog!

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