Nithya Mahadevan

It is hard to understand Fabric's long-term strategy. On one hand, it was smart to build it around PBI, the leading BI platform. On the other hand, the key to Power BI's success in dethroning Tableau was its' extremely affordable licensing. Competitive licensing is not an element that Fabric's always-on pricing model can boast of at current rates, especially when the likes of Databricks, Snowflake, and other rising platforms are around. Perhaps Microsoft will be content with Fabric always being the 3rd/4th best option?

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We’re learning a lot at Snowflake, very quickly, about what it actually takes to deliver high quality, high-performance agentic analytics in the real world. Rajhans Samdani and I collaborated to write this latest post, which captures some of those learnings, especially around the emerging “context layer” and why it is becoming foundational for trustworthy data agents. https://lnkd.in/g-8TMcmD What’s been most interesting is how consistently the same themes show up across customer engagements. Whether it’s Semantic Views, Cortex Analyst, our Agent Framework, or Snowflake Intelligence, the same pattern keeps reinforcing itself. You can’t get to reliable, scalable outcomes without a strong, well-defined layer of context that connects data, business meaning, and user intent. This is one of those moments where the pace of change is hard to overstate. The space is evolving quickly, and we’re seeing ideas move from early experimentation into real customer impact in a matter of weeks. Being able to take what we’re learning directly from customers, feed that back into the platform, and then see it improve results is what makes this work so exciting right now. If you’re thinking about how to build or scale agentic analytics, I hope you’ll find this perspective useful.

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Truly transformative. Within the next 6 months coding will be fully commoditized. Only design, distribution, and hardware will be the moat. It is time to lean in #AI #FutureofWork #OhioTech

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Less than a week to go🔥 After the incredible response to our MCP, Agentic AI, and Vibe Coding sessions, Arvind Narayanamurthy and I are kicking off a brand-new workshop series on one of the most requested and practical topics- Retrieval-Augmented Generation (RAG). The first installment of this series is specifically designed for beginner to intermediate learners, offering a comprehensive introduction to the fundamental concepts of RAG. 🔍 Whether you're building production-grade AI apps or just starting to explore how LLMs can go beyond their training data - RAG is a must-have skill. This isn’t your usual high-level webinar. We’ll take you through real implementation, live guidance, and building from scratch You’ll: ✅ Learn how RAG improves LLM performance with fresh, contextual data ✅ Understand vector DBs, indexing, and retrieval techniques ✅ Get Hands-on: Build and deploy a working RAG pipeline using LangChain + Pinecone ✅ See no-code and low-code demos for faster experimentation ✅ Explore real-world use cases and performance tips 📅 Reserve your seat, roll up your sleeves, and let's get building. 👉 https://lnkd.in/gS_B7_7d

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𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗜 𝗺𝗮𝗿𝗸𝗲𝘁 𝗯𝗲𝗲𝗻 𝘂𝗽 𝘁𝗼 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝟮 𝘄𝗲𝗲𝗸𝘀 𝗼𝗳 𝟮𝟬𝟮𝟲? Here are the emerging trends in the data and AI talent landscape based on my meetings with Heads of AI and CDOs along with a note on the churn at C-level ✅ High demand for hands on data and AI engineers which is also driving higher salary packages ✅ High demand for data scientists that have put GenAI products and solutions into production - this is fueled to a certain extent by AI CoEs ✅ High demand for AI governance specialists ↘️ Low demand for data scientists that have limited experience with putting AI solutions into production ↘️ Low demand for hands off managers ↘️ Low demand for pure data analysts. While the head of AI/ CDO function is still in its infancy in many organizations. Which means, data/ AI maturity is low especially from a culture perspective. However, expectations on what these teams need to deliver are sky high because of the narrative built externally based on how companies want to be perceived by their shareholders/ customers. As a result, the pressure on these teams and their leaders is immense. This often leads to a revolving door situation for CDO/ head of AI roles. The prediction I made late last year regarding change of guard at the CDO level is already playing out in many banks and FS companies. ♻️ If you found the insights valuable, please share it so that people from your network can benefit 📍 Except for the image, no AI was used to create this post!

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Launching: Chat with Priyank (beta) today on https://priyank.tech A few weeks ago, I launched priyank.tech as a single home for my work, projects, and notes. Today I’m adding a faster way to get to the signal: Skip the scrolling, ask.  “Chat with Priyank” answers questions about my projects, views, and skills by pulling the most relevant context and giving you a concise, useful response. What’s under the hood (and why): - Human‑verified access - Conversations start only after the system knows it’s interacting with a person, not a bot. - Scope & guardrails - It only answers questions about me. When a query is out of scope or uncertain, it responds with a polite “I don’t know.” - Context engineering first (no RAG, yet!) - An analysis by industry veterans suggested that GPT-5 class models can correctly use ~85% of the provided context. I want to see how far careful, prompt, and context design can go before adding retrieval. I’ll evaluate this over the next few days and decide before graduating from beta. - Prompt caching enabled - To keep costs under control (and responses snappy), repeat prompts and shared context are cached. - Early memory layer - A lightweight short-term memory layer powers more personal, consistent answers across sessions. Try it on: https://priyank.tech   If it’s your first visit, do subscribe to my newsletter—I’ll share build notes, decisions, and lessons learned there. Starter prompts: “Tell me about your AI and Generative AI expertise “How can you help me become an AI first organization?” Feedback is welcome, especially edge cases and failure modes.  #AI #product #buildinpublic #LLM

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Quantization in Large Language Models (LLMs) As LLMs scale, intelligence is no longer the bottleneck — memory, latency, and cost are. Quantization turns out to be one of the most powerful (and misunderstood) techniques enabling real-world deployment of large models. In this blog, I break down: • Why LLMs are surprisingly quantization-friendly • INT8 vs INT4 vs NF4 — what actually works in practice • How modern architectures absorb low-bit noise • Where quantization fails and how to design around it Quantization is no longer an optimization step — it’s foundational infrastructure for production LLM systems. Link: https://lnkd.in/gqGje75G #LLMs #AIEngineering #Quantization #MachineLearning #GenAI #MLOps

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15 years back, when we were building voice bots for the US market - one of the key debates used to be whether to declare or not to declare its a bot. Even if not explicitly say things like "I can help you with X,Y, Z queries and you can say things like .... " indicating a limited understanding. Even if the users did not understand what a virtual assistant is - they understood this is not a human being speaking on the other side. European regulations strictly mandate the voice agents to declare before proceeding in a conversation. India is an upcoming market but getting into the space at different stage of maturity of conversational engines - Hence there is a lot of asks to cover up the underlying bot and not by any means show up its a machine. The experience should be "human like" but that does not mean we should hide the machine behind. For an end consumer, its important to know the species they are speaking to and I now see why some of the western paradigms firmly support the declarative approach. Its just more human.

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