Software engineers often think adding AI is just a simple API call, but moving from a Proof of Concept to a stable production system requires a completely different mindset. Maria Vechtomova breaks down the harsh reality of MLOps, why rigorous evaluation is non-negotiable, and why autonomous agents are riskier than you think.In this episode, we cover:The essential MLOps principles every software engineer must learnHow to bridge the gap between a demo and a production-grade solutionStrategies for evaluating agents and detecting model driftThe security risks of customer service agents and prompt injectionPractical tips for using AI tools to boost your own productivityConnect with Maria:https://www.linkedin.com/in/maria-vechtomovaTimestamps: 00:00:00 - Intro 00:01:25 - Why the AI Hype Was Actually Good for Monitoring 00:03:07 - Real-World AI Use Cases That Deliver Actual Value 00:05:16 - MLOps Basics Every Software Engineer Needs to Know 00:08:08 - The Hidden Complexity of Deploying Agents to Production 00:12:02 - Minimum Requirements for Moving from PoC to Production 00:15:41 - Step-by-Step Guide to Evaluating AI Features Before Launch 00:18:08 - How to Handle Data Labeling and Drift Detection 00:21:55 - Why You Likely Need Custom Tools for Monitoring 00:24:56 - Why Engineers Build AI Features They Don't Need00:26:01 - How Software Engineers Can Learn Data Science Principles 00:31:36 - The Dangerous Security Risks of Autonomous Customer Service Agents 00:34:44 - Why Human-in-the-Loop is Essential for Avoiding Reputational Damage 00:36:18 - Boosting Developer Productivity with Opinionated AI Prompts 00:39:20 - Using Voice Notes and AI to Organize Your Life#MLOps #SoftwareEngineering #ArtificialIntelligence

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