Ankur Jain

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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If your datasets are poorly labeled, you’re inviting a host of issues like noise, bias, and inconsistencies that can seriously hurt your model's accuracy and reliability when it’s out in the wild. That's why data labeling has evolved from being just a preliminary step into a vital part of engineering and operations in the AI lifecycle.

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CRO isn’t one tool it’s a stack working together. Analytics with Google Analytics 4, Heap Analytics, and Mixpanel shows what users do. Behavior tools like Hotjar, Smartlook, and FullStory reveal why they do it. Experimentation platforms such as Optimizely, VWO, and AB Tasty test what works. Add landing pages, AI personalization, and revenue tools like HubSpot and Kameleoon and you turn insights into conversions. CRO Stack = Data + Behavior + Testing + Personalization → Revenue #CRO #ConversionRateOptimization #GA4 #GrowthMarketing #ABTesting #DigitalMarketing #MarketingAnalytics

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AI won't replace analysts. But analysts who use AI as a thinking partner will replace those who don't. Three of the sharpest data people I know just launched a course to help you become that analyst. Sravya (Senior Manager, Data Science @ Grammarly), Hai Guan (Head of Data @ Ontra), and Shane Butler (Principal Data Scientist @ Ontra) created AI Analytics for Builders on Maven. 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐜𝐨𝐮𝐫𝐬𝐞 𝐭𝐞𝐚𝐜𝐡𝐞𝐬: → Translate vague product questions into clear, testable hypotheses → Define primary, secondary, and guardrail metrics with AI → Break down KPI movements through segmentation and trend analysis → Pressure-test your assumptions and edge cases → Turn analysis into clear, actionable recommendations 𝐓𝐡𝐞 𝐝𝐞𝐚𝐥: 🔥 5 early bird spots at $180 (normally $1,200) 🎟️ Everyone else who applies gets 40% off ⏰ Applications close in 24 hours I've worked with these folks. They're sharp, practical, and actually care about teaching skills that stick. If you're in data, product, or analytics, this is worth a look. 👉 𝐀𝐩𝐩𝐥𝐲 𝐡𝐞𝐫𝐞: https://lnkd.in/g7AHwSYY ♻️ Repost to help someone in your network level up their analytics game

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As Databricks adoption accelerates, platform leaders face mounting pressure to deliver reliable performance, predictable costs, and scalable data operations. But with a fragmented vendor landscape and no one-size-fits-all approach, selecting the right observability solution can feel overwhelming. That's why we created the Databricks Data Observability Buyer's Guide - a clear, actionable framework to help you cut through the noise. 📥 Download your free copy today: https://lnkd.in/gayN9ui9 #Databricks #DataObservability #FinOps #AIOptimization #CloudCosts

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How a 7B Model(SLMs) Beat GPT-4o(LLMs) in Agentic Workflows 🤯📉 We often assume that bigger is better when it comes to LLMs, especially for complex reasoning tasks. In my latest video, I break down AgentFlow, a new framework where a 7B parameter model outperforms top-tier proprietary models (like GPT-4o) on complex, multi-turn agentic benchmarks. Here’s what I cover in the deep dive: 🔹 From Monolithic to Modular: Most tool-using LLMs try to do everything (reasoning + tool calling) in one monolithic process. AgentFlow decomposes this into four specialized modules: Planner, Executor, Verifier, and Generator, all coordinating through an evolving memory. 🔹 The "Secret Sauce": Flow-GRPO: The real innovation is in the training. Traditional offline Supervised Fine-Tuning (SFT) often leads to performance collapse in these systems. They introduce Flow-GRPO (Flow-based Group Refined Policy Optimization), a method that trains the "Planner" agent inside the multi-turn loop. It solves the sparse reward problem by broadcasting the final outcome reward back to every step of the reasoning chain. 🔹 The Results are Wild: By optimizing the planner "in-the-flow," their 7B model achieved massive gains over state-of-the-art baselines: +14.9% in Search tasks +14.0% in Agentic reasoning +14.5% in Math Crucially: It outperformed the ~200B parameter GPT-4o across all these domains. This is a must-watch if you are building AI agents or interested in how we can make smaller models significantly smarter through better system architecture and training. 📺 Watch the full breakdown here: https://lnkd.in/gSfPHbwh Github : https://lnkd.in/g4RaEcFg #AI #AgenticAI #LLM #MachineLearning #AgentFlow #OpenSource

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🚀 Big news for Databricks Assistant users — AI just got way better at editing your code. Introducing Assistant Edit Mode 🧠💻 — a new way to apply AI-generated suggestions across multiple cells at once. Now you can: ✅ Refactor logic, rename variables, and clean up code — all with a single prompt ✅ Migrate SQL, Pandas, or Python code faster and more accurately ✅ Standardize style, remove clutter, and even generate test scaffolding automatically No more tedious, cell-by-cell edits. Edit Mode understands your entire notebook context — so your changes are consistent, fast, and easy to review. In early testing, it cut refactoring time by over 50%. This is just the start. You will soon hear about Edit Mode in AI/BI Dashboards and how we are integrating agent-like workflows into Databricks Assistant. Stay tuned! 📖 Read the full blog and see it in action 👇 #Databricks #GenerativeAI #DataScience #MachineLearning #DataEngineering #Productivity #AI

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