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A Practical Guide

Building AI systems users love isn't just about great models or slick interfaces - it's about bringing user-centered design and machine learning engineering together seamlessly. Yet, with 54% of AI projects failing to reach production and only 18-36% achieving expected benefits (Deloitte, 2024), we need a better approach.

Who This Guide is For

Product Managers: Learn to coordinate UX and ML workflows
UX Designers: Understand how ML capabilities shape design decisions
ML Engineers: See how user needs influence model development
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mlops-vs-design-cycle-iteration-monitoring.jpg
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Table of Contents

📌
The Problem
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Why bridging design and AI is a challenge worth solving.
🤝
Two Workflows, One Goal
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How design thinking and MLOps complement each other.
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Discovery
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Learn how to understand user needs and map data to create a strong foundation.
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Ideation
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Turn insights into actionable ideas and validate them with experimentation.
🛠️
Prototyping
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Dive into building, deploying, and aligning functionality with user needs.
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Feedback
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Optimize systems through feedback and real-world performance.
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