Rahul Garg

Join us for an exclusive webinar sponsored by Google Cloud and NVIDIA on June 27 at 11AM ET! As enterprises navigate hybrid and multi-cloud landscapes, the management of networks becomes pivotal. Discover how AI is reshaping business agility and learn key insights from industry leaders Muninder Singh Sambi of Google Cloud and Neelay Shah of NVIDIA. Don't miss this 30-minute session to uncover strategies for securing, streamlining, and scaling your network effectively. Register now for actionable insights and practical advice.

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IBM THINK is happening now in Boston…. Enterprise Customers are firmly supporting a number of key priories regarding AI. 1). TRUST is critical. Both the data used to train models and the governance used to ensure they act consistently in accordance with policies, intent and regulatory environments. 2) OPEN provides critical flexability to use the right model - for the right use-cases. 3) ECONOMICS matter - to go from pilot to production requires both a compelling aggregate business case and efficient inferencing unit-costs.

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Purpose-specific Gen AI models, such as SLMs, can often address client requirements more effectively than general-purpose LLMs, which may not always provide the precise solutions needed for specialized tasks. For example, MiniCPM delivers superior OCR performance compared to Google Document AI in specific use cases.

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🤔 Unlock revenue tied to data streaming or keep your focus on the rest of your roadmap? Good news: that’s a choice you no longer need to make—the Confluent OEM Program is here to empower you to do both! MSPs, CSPs, and ISVs, don’t miss out—learn more about the program on our blog!

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Microsoft's AI model, Aurora, quickly predicts levels of six major air pollutants worldwide, including carbon monoxide and particulate matter. Read more in this article from Nature. #msftadvocate #ai https://lnkd.in/g-AppzdB

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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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Here is a blog post that explains how to use OCI Generative AI to perform Retrieval Augmented Generation. The article focuses on various hyper-parameters that can be tuned. https://lnkd.in/ghP5r6ef #GenerativeAI

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Give your AI models an edge. Leaders across industries are racing to get generative AI off the ground—and get ahead of the competition. How can your enterprise unlock the full potential of gen AI? Achieve more accurate and nuanced results by moving beyond generic training sets and grounding your AI models in real-time and enterprise data. At Google Cloud, we call this approach “enterprise truth”. Grounding foundation models in enterprise truth—like your databases, enterprise applications, and Google Search—enables the real-world integration, data authenticity, and data relevance today’s business demands.

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New Video: The role of Generative AI in Integration https://lnkd.in/gQWd5Pgm #LogicAppsAviators

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Unveiling IBM Concert: Revolutionizing Application Management with AI Automation TL;DR - Since Arvind Krishna became IBM’s Chairman and CEO, IBM has pivoted toward hybrid cloud and AI, focusing on software and consulting for sustainable growth. The strategic spinoff of Kyndryl allowed IBM to streamline operations and emphasize annual recurring revenue (ARR). At Think 2024, IBM unveiled IBM Concert, an AI-powered automation tool enhancing application management. IBM Concert offers AI-driven insights, addressing security, compliance, and certificate management, aligning with IBM's broader automation strategy. Read my full analysis 👇 https://lnkd.in/eUdRNrXW #IBM #AI #Automation #ApplicationManagement

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Data marts, warehouses, lakes, whatever the name, the pattern of consolidating data across an enterprise for comprehensive analytics is a familiar one. Every enterprise has many data sources, some operational, some analytics, some telemetry, and bringing them together has always been a challenge. Cleaning the data, curating for consistency, exploring, doing data science, building reports, monitoring, is another series of challenges in bringing a range of tools to bear on the data, thanks to diverse data formats and tooling. With Gen AI, the requirements (e.g., vector indexing for RAG) and potential value of the data have both gone up. And the pressure to ensure right use of the data, to comply to a broad range of regulations, to secure the data has never been higher. We believe the time has come to come together around open data standards to ensure that data is easy to consolidate and easy to plug into a wide variety of tools, all in a properly governed way. Josh and I describe the approach we are taking at Microsoft with Fabric and Purview. We are building on open Parquet-based updatable table formats such as Delta, Iceberg and Hudi, and interop across them with XTable. We are making it easy to consolidate data with mechanisms like mirroring and shortcuts. Ultimately, users should be able to use their tool of choice for a given task with the least friction.

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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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With MongoDB, we help engineering leaders get off the modernization hamster wheel and embrace predictable, optimised spending that fuels their most ambitious initiatives. With powerful features like automated cluster sizing, on-demand pausing, and granular spend visibility, MongoDB Atlas helps you right-size your data infrastructure and take complete control of your IT costs. Perfect way to break free from the constraints of economic uncertainty and get easier budget approvals. This blog helps you understand how MongoDB Atlas can help you take better control of your data costs. #MongoDB #cloud #innovation

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Microsoft's AI model, Aurora, quickly predicts levels of six major air pollutants worldwide, including carbon monoxide and particulate matter. Read more in this article from Nature. #msftadvocate #ai https://lnkd.in/g-AppzdB

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🎉 @New Relic AI monitoring, the industry’s first APM for AI, is GA! New Relic AI monitoring empowers engineers with visibility and insights across the AI stack, making it easier to troubleshoot and optimize AI apps for performance and cost—all while ensuring data privacy and security. Get started:

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Cloud is not slowing down anytime soon! Microsoft's Cloud business is growing at an impressive 20% y/y even though it's already at $24B. AWS is growing 19% y/y ($27.5B) GCP is growing faster by 35% y/y albeit at smaller scale ($11B)

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When I speak to customers on AI, they often ask where do you even start? This was a great conversation of matching a project to the AI resources. We also dive a bit into what it takes to build an AI Cloud

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VoiceRAG: retrieval-augmented generation (RAG) apps with a real-time voice interface. The new GPT-4o realtime API for audio is now available in Azure OpenAI. This model allows developers to build applications with user interfaces like ChatGPT’s advanced voice mode, where users have a real-time, full-duplex audio channel with the model. If the model uses audio for input and output, how do you integrate a retrieval system to ground responses, along the lines of what typical RAG applications do? In this short write up we present a simple pattern for this we called VoiceRAG. The blog post includes a pointer to a repo with a sample implementation of the VoiceRAG pattern you can clone and try with our data or your own. https://aka.ms/voicerag

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