AI Engineering Does Not Equal Machine Learning

Antonija Bilic Arar

Do you think you know what AI engineering is?

Tejas Kumar, AI DevRel Engineer at DataStax, thinks the main problem with AI engineering is that it lacks a formal definition. Many people confuse it with machine learning, research, and engineering, but those are completely different skills.

AI engineering does not require academia, experience with machine learning models, Python, or linear algebra: it’s just applying AI to solve problems.

According to Tejas, one can do this without ever training anything; instead, one can just make a network request to an AI API and use the returned output to solve problems.

This video is a part of ShiftMag’s video series, Engineer Explains.

We’ve asked experienced engineers to share how they would explain some basic and some less basic tech terminology to different tech job titles or at three levels of experience — from junior developer to CTO.

More videos from the Engineer Explains series:

Test Driven Development

Refactoring Legacy Code

Agile Software Development

Career Tips for Tough Times ft. ‪Pragmatic Engineer‬

OpenTelemetry and Observability 2.0

Feature Flags Explained

JAMstack Explained

Observability Explained

Large Language Models Explained

DevOps Explained

DevRel Explained

Network APIs Explained

Verifiable Credential Explained

Mob Programming Explained

Machine Learning Explained

RUST Explained

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