Shrey Shah

AI agents are only as powerful as their memory. Redis has been named the #1 data storage choice for AI agents in 2025. The reason? Redis delivers what AI truly needs: - Real-time vector storage - Semantic caching - Long-term memory for LLMs At Bassirah, we see this as validation of why Redis is central to the next generation of AI-native data platforms powering everything from agentic RAG systems to enterprise decision engines. 🔗 Read more: https://lnkd.in/db47k6zG #AI #Redis #RAG #LLM #DataEngineering #AIAgents #ArtificialIntelligence

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The Databricks Startups team answers founders’ most common AI questions—where to start without hype, how to stay compliant with sensitive data, what to build versus buy, how to cut hallucinations, and how to keep costs in check. 💡 Get step‑by‑step starting points, governance patterns for sensitive data, a pragmatic build‑versus‑buy lens, tactics to curb hallucinations, and cost controls that scale. 🔗 Full post: https://lnkd.in/ehRa3Yp5 #Databricks #DatabricksForStartups #AI #GenAI #MLOps

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If your team is building with LLMs, you know the value of clean, retrievable data. That's why we're teaming up with Unstructured to streamline one of the hardest parts of AI development. This integration removes friction across the AI stack—from unstructured ingestion to semantic retrieval—so your teams can focus on building. Read more from our Product Marketing Managers, Rini Vasan and Jim Allen Wallace, and Unstructured’s Head of Developer Relations, Maria Khalusova here: https://lnkd.in/e98QqXAB

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LLMs are powerful, until they melt your GPU budget and slow everything down. Today, Hrittik Roy and Aditya Soni will break down how they turned an unpredictable, resource-hungry LLM deployment into a fast, efficient, production-ready stack using CNCF tools. They’ll walk through: - Serving LLMs reliably with KServe + Kubeflow - Auto-scaling with Volcano and KEDA - Tracking performance + drift with Prometheus, Grafana & OpenTelemetry If you’re running LLMs in production... or trying to, this is a session worth joining. 🕒 2:30–3:00 PM EST 📍 Thomas Murphy Ballroom 4 | Level 5 #KubeCon #CloudNativeCon #vCluster

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Ruokai Yin, our summer intern from Yale to Microsoft Azure, worked with Xuan Zuo, @Sattwik Deb Mishra, Hokchhay T. and Preyas Shah and me. We published a paper based on his project: https://lnkd.in/eHuG3BSt Summary of paper: Finding optimal sharding is hard especially for large LLMs. A large number of devices have to be divided into TP, PP, EP dimensions. For TP, we have to decide the tensor dimension e.g. head, hidden size to shard for every op. Additionally, TP can be one-dimensional/two-dimensional etc. This makes for a large sharding space that is hard to cover exhaustively. SoTA methods use heuristics to select sharding e.g. GSPMD, Sequence Parallelism, Helix etc. There are significant time gaps, months or years, between the discovery of each of these methods. We want to fast-forward the process of discovery by 1) using a formal representation of the sharding space, and, 2) using reinforcement learning to search the sharding space.    This paper demonstrates that our method is 3.5x better than random search, and on par with hand-tuned heuristics when looking only at 1D sharding and without allowing sequence parallelism. We aim to extend the work to multi-dimensional sharding as well as sequence parallelism. Beyond that, we wish to combine the problem of sharding with fusion, tiling and collectives as well.

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Reveal 1.8.0 introduces two major upgrades that make building embedded analytics faster, smarter, and more scalable: Conditional Formatting and the Redis Cache Provider. These updates help developers, data teams, and enterprise users deliver clearer insights and higher performance with less complexity. Whether you are enhancing dashboards with color logic or optimizing query speeds for high traffic applications, Reveal continues to set the standard for customizable embedded BI that simply works: https://lnkd.in/dh3SgXp8

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https://lnkd.in/gwr_aAYk 🚀 Unlocking the Power of LLMs: It's All About the Ecosystem! Think of a Large Language Model (LLM) like a database: you send a query, you get a response. But unlike deterministic, structured databases, LLMs are stochastic, unstructured, and can sometimes hallucinate. The real magic? The ecosystem around the model that makes it reliable and impactful. Here’s a simple AI adoption strategy: treat the LLM as a black box that can produce brilliant responses or hallucinate. Control outcomes by: Pre-processing queries: Craft clear intent with cumulative context and guardrails (content moderation, jailbreak detection, sensitive data filtering). Post-processing responses: Evaluate and re-rank outputs for relevance, safety, and accuracy. Building a feedback loop: Refine prompts and learn from outputs to improve accuracy over time. The full AI interaction loop: User → Query → LLM → Response → Feedback → Next Query Each step is an opportunity to enhance safety, context, and quality. From human or AI agents wielding tools (memory, retrieval, orchestration) to red teaming and mechanistic interpretability, a robust LLM pipeline ensures reliable, responsible AI. How are you building your AI ecosystem? #AI #LLM #MachineLearning #PromptEngineering #ResponsibleAI #GenerativeAI #NLP #ContextEngineering

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What happens when you pair LangGraph’s flexibility with Snowflake’s secure, scalable compute? You get a seamless path to production-grade LLM applications. Fabian Hernandez, part of our Infostrux team, walks you through how LangGraph agents can now be executed entirely inside Snowflake using its new LLM function framework. It’s an exciting step forward for anyone building: -LLM agents -Retrieval-augmented generation pipelines -Conversational workflows directly tied to enterprise data This article is a must-read for teams operationalizing AI where it matters most—in the cloud, with governance built in. Explore it here: 👉 https://hubs.ly/Q03ykwlj0 #Snowflake #LLMFunctions #LangGraph #AIWorkflows #GenAI #EnterpriseAI #Infostrux

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