Analytics Vidhya

The stage is yours. 🎤 If you're building what others are still theorizing about, we want your voice at India's largest AI conference. - The DataHack Summit 2026 📅 August 5–8, 2026 📍 The Leela Bhartiya City, Bengaluru 👥 Thousands of AI practitioners, researchers & builders in one room This is your platform to: → Share breakthroughs that matter → Shape how India's AI community thinks and builds → Stand alongside the sharpest minds in the field We're looking for talks on: 🔹 Generative AI & LLMs 🔹 Agentic AI Systems 🔹 MLOps & AI at Scale 🔹 Responsible & Trustworthy AI 🔹 Multimodal AI, RL & beyond The Call for Speakers is OPEN. 🚀 Don't wait for someone else to tell your story. 👉 Apply now: https://lnkd.in/gs5gyB-w Speaker lineup: Abhilash Majumder, Abhishek Kumar, Alessandro Romano, Anuj Saini, Arun Prakash Asokan, Bhaskarjit Sarmah, Dipanjan S., Dr. Aditya Bhattacharya, Sayan Ranu, Hardik Meisheri, Harshad Khadilkar, Hitesh Nayak, Joinal Ahmed, Kartik Nighania, Kunal Jain, Logesh Kumar Umapathi, Manoranjan Rajguru, Manu Joseph, Mayank Baranwal, Nikhil Rana, Nitin Agarwal, Praneeth Paikray, Pranjal Singh, Prateek Khandelwal, Rohan Rao, Sanathraj Narayan, Sandeep Singh, Saurav Agarwal, Sonny Laskar and Tanika Gupta. #DataHackSummit2026 #DHS2026 #AnalyticsVidhya #AIAgents #AgenticAI #GenerativeAI #DataScience

Ever found yourself stuck trying to query large datasets, with limited tools that just don’t scale? 💭 When dealing with massive amounts of data stored on AWS S3, it can be a real challenge to run efficient queries without blowing through resources or wasting time. That’s where Amazon Athena comes in. 🔥 Athena allows you to run SQL queries directly on data in S3 without the need for complex infrastructure. It's fast, cost-effective, and can handle big data with ease. But getting started? That's often the tricky part. This is why we created the AWS Data Querying with S3 & Athena course, led by JATIN GOEL. https://lnkd.in/gWXXNP2S In this free course, Jatin walks you through everything from setting up Athena to writing optimized queries, offering clear, practical lessons that you can apply right away. If you want to streamline your data querying process and take full advantage of AWS, this course is a must! 💡 #AWS #Athena #S3 #DataQuerying #CloudComputing #TechSkills #FreeCourse #Learning

💡 Your enterprise is sitting on a goldmine of data. But if your teams can't find it, it might as well not exist. In today's data-driven organizations, teams spend a significant amount of their workweek searching for information that should be instantly accessible , resulting in lost productivity, delayed decisions, and slower onboarding. The root cause isn't a lack of data. It's a retrieval problem. In our latest article, we explore how Snowflake Cortex Search is redefining enterprise search through hybrid retrieval, combining keyword precision with semantic understanding to deliver fast, accurate results at scale. From powering RAG applications that ground AI responses in real enterprise data, to building natural language search interfaces embedded directly into your workflows, Cortex Search turns retrieval into a strategic business capability. If your teams are still guessing search terms instead of finding answers, it's time to rethink your retrieval layer. 🔗 Read the full article to see it in action. https://lnkd.in/gd_XmrHK #EnterpriseSearch #Snowflake #CortexSearch #RAG #AIApplications #DataStrategy

Writing the same prompts over and over? There's a better way. Claude Code's Skills Architecture lets you build once and reuse everywhere — turning institutional knowledge into repeatable, scalable AI capabilities across your entire team. Swipe through to learn how ➡️ Ready to go deeper? Our Claude Code Mastery program teaches you to build production-grade AI systems from the ground up. 🔗 Enroll here: https://lnkd.in/g6237A8z #ClaudeCode #ClaudeAI #AnthropicAI #AITools #GenerativeAI #LLM

What separates a basic chatbot from a production-ready enterprise AI agent? The answer lies in architecture, orchestration, and deployment and that's exactly what this workshop is built around. We're excited to share this full-day hands-on workshop at DataHack Summit 2026: 🚀 Enterprise Agent Systems: From Foundations to Deployment Led by Manoranjan Rajguru, AI Architect at Microsoft, you will learn: ✅ AI Agents on Azure & Foundry Agent Service ✅ MCP Servers & Tools Integration ✅ Multi-Agent Orchestration & Workflows ✅ Production Best Practices, Governance & Deployment Whether you're an AI engineer, architect, or tech leader; this is the deep-dive you've been waiting for. 📅 Aug 08, 2026 | ⏰ 09:30AM – 05:30PM | 📍 Bengaluru 🔗 https://lnkd.in/gHef3XFA #AIAgents #Azure #DataHackSummit2026 #MicrosoftAI #EnterpriseAI #AnalyticsVidhya

Most n8n workflows hit a wall when they get too big. 🧱 The fix? Sub-workflows. Instead of building one massive, hard-to-manage workflow, you can break it into smaller, modular pieces and call one workflow from another using the Execute Sub-workflow node . Here's why this is a game-changer: → Keeps your automations clean and reusable → Easier to debug, trace execution from parent to sub-workflow and back → Solves memory issues on large workflows → Sub-workflow executions don't count toward your plan limits T he PDF above walks you through exactly how to set it up; from creating the trigger node to passing data between workflows seamlessly. If you're building serious automations in n8n, sub-workflows aren't optional. They're the architecture. Want to go deeper into n8n and build real-world AI-powered automations? 🎓 Check out our free course on n8n: https://lnkd.in/gAdwwFaz Drop a 🔁 if you found this useful, and share it with someone building in n8n!

Claude can write code. But what if it could also query your database, read your JIRA backlog, pull live errors from Sentry, and commit a fix; all from a single instruction? That's what MCP makes possible. Model Context Protocol is the open standard that connects Claude to external tools, data sources, and workflows in one unified architecture. Think of it as USB-C for AI. Here's what's inside this carousel: 🔹 What MCP is and how its architecture works 🔹 How Claude connects to tools via stdio and Streamable HTTP 🔹 Real integrations — JIRA, PostgreSQL, Sentry 🔹 Security threats: prompt injection, tool poisoning, and rug pulls 🔹 How to configure GitHub, Postgres, and web search in parallel 🔹 Real enterprise breach case studies and governance lessons If you're building with Claude Code, MCP is the layer that turns it from a coding assistant into an active agent. Want to go hands-on? We have a free Claude Code course to get you started 👇 https://lnkd.in/g9RjQYnA Save this if you're building AI agents 🔖

We have a free course on Building AI Agents with Amazon Bedrock AgentCore and if you're working on agents, this one is worth your time. A lot of people can build AI demos. Very few know how to take agents into production with memory, monitoring, and scale. That's exactly what this course focuses on. In this course, you'll learn how Amazon Bedrock AgentCore works in real-world setups: ✅ How agent runtime, memory, and observability fit together ✅ How to deploy production-ready AI agents in minutes using AgentCore Runtime ✅ How to add long-term memory so agents retain user context across sessions ✅ How to observe agent behavior with built-in tracking for latency, tokens, errors, and workflows What's inside the course: ✅ Introduction to Amazon Bedrock AgentCore ✅ Bringing AI agents into production in minutes with AgentCore Runtime ✅ Deploying AI agents with long-term memory ✅ Observing agent applications using AgentCore Observability The course is taught by Elizabeth Fuentes Leone and Vikash Agrawal from AWS, bringing hands-on, production-first perspectives straight from the source. If you're building AI agents and want them to behave like real products, not experiments, this one's for you: https://lnkd.in/gr-VhA4u

Claude Code and Codex look the same from the outside. Same npm install. Same natural language prompts. Same goal. But the moment you go deeper, they split completely. And even the vocabulary is different: → CLAUDE.md vs AGENTS.md → Auto memory vs Explicit memories system → Checkpoints + /rewind vs Code reviews + structured state → Inline iteration vs Worktrees + review-driven workflows → Remote Control (local desktop) vs Cloud delegation via web → Session-centric vs Distributed, system-oriented → Built-in parallel agents vs Parallelism via orchestrated worktrees Same concept. Completely different world. Which one do you like better? Share in the comments. #ClaudeCode #Codex #AIEngineering #DevTools #AICoding

3 cutting-edge AI papers that were released last week:   ➡️ Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items – This paper introduces a commercial-grade virtual try-on system, balancing cost and quality for real-world e-commerce. It shows how to deploy advanced AI in practical applications. 🔗 Full Paper: https://lnkd.in/gkYDWAMT   ➡️ LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model – This work presents a unified model for both understanding and generating multimodal content. It achieves state-of-the-art performance on several benchmarks. 🔗 Full Paper: https://lnkd.in/gvxDgA7g   ➡️ AgentSPEX: An Agent SPecification and EXecution Language – This paper introduces a new language for specifying and executing LLM-agent workflows with explicit control. It provides a more structured approach to LLM-agent workflow specification. 🔗 Full Paper: https://lnkd.in/g-Vs5afU   Which breakthrough excites you most? Let's discuss! #AI #MachineLearning #Research #NLP #ComputerVision #MultiModal