Siddhartha Vemuganti

A Clear-Eyed Look at Emotion AI: Current Reality and Future Questions

AI/ML, Data Engineering /

The tech world loves a bold prediction. If you’ve been following the AI space lately, you’ve likely seen headlines proclaiming that machines will soon understand human emotions better than we do. Having researched and implemented AI systems, I’ve learned to look past the hype and focus on what’s actually possible today. The Reality of Emotion […]

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Smarter RAG Retrieval with Multi-Attribute Vector Indexing

AI/ML /

Introduction Retrieval-Augmented Generation (RAG) has transformed AI applications by enhancing Large Language Models (LLMs) with real-world, dynamic data. However, traditional vector search methods often struggle with complex queries that involve multiple attributes, such as time sensitivity, location, or category filtering. For example, if you ask a RAG-powered system for “recent clinical trials on diabetes in

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Smarter RAG Retrieval with Multi-Attribute Indexing

AI/ML /

Precision AI search, unlocked! In Retrieval-Augmented Generation (RAG) applications, efficiently handling complex queries that involve multiple attributes is crucial for delivering accurate and relevant results. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: Traditional vector search methods often fall short when processing queries that require filtering based on multiple attributes, leading to less precise retrievals. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗠𝘂𝗹𝘁𝗶-𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗲 𝗩𝗲𝗰𝘁𝗼𝗿 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴: By

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How to Build an AI That Actually Does Things (Not Just Talks) – Agentic AI Service – A Simple Guide for Leaders

AI/ML /

Most AI systems today are like really smart assistants—they give you recommendations, but they still wait for you to act. But what if AI could make decisions and take actions on its own? That’s what Agentic AI is about. It doesn’t just respond—it gets things done. Think about customer support. Most AI chatbots just answer

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Data Teams Break When Models Crash​

Data Engineering /

Engineers aren’t the problem…A recent McKinsey study revealed that misalignment between technical teams and business stakeholders causes more project failures than technical challenges. Here’s what I’ve learned leading cross-functional data initiatives:Translation Isn’t Optional ↳ Your data engineers speak schemas and pipelines, while your business team speaks KPIs and outcomes. When a business requests “real-time analytics,”

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Breaking Through: When Your Career Feels Stuck at 99%

Philosophy /

Ever feel like your career progress has hit a wall? You’re not alone. Many professionals experience this plateau…– Conduct a skills audit: Identify and bridge critical gaps in your toolkit– Challenge self-limiting beliefs: Reassess and update your perception of your capabilities– Protect your growth time: Set clear boundaries to prioritize personal development– Diversify your focus:

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A human hand with tattoos reaching out to a robotic hand on a white background.

The Silent Revolution in Enterprise AI

AI/ML /

One SQL Query Changes Everything…Databricks introduces simple, fast, and scalable batch LLM inference on Mosaic AI Model Serving (Oct 2024). Transform unstructured data processing with a single SQL query. 5 Game-Changing Capabilities of Batch AI Processing:SQL-First Revolution↳ Yesterday: Complex Python pipelines↳ Today: SELECT ai_query(‘llama-70b’, text) FROM documents ↳ Impact: 90% less complexity, zero maintenanceDual-Mode Intelligence↳

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