Why Fine-Tuning is the Secret Sauce for ML Engineers in 2025

Author(s): Mehdi Zare

Originally published on Towards AI.

Why Fine-Tuning is the Secret Sauce for ML Engineers in 2025

I’ve worn many hats in my career — from writing code at a software startup, to crunching numbers in finance, advising clients as a consultant, and even dabbling in marketing analytics. Through it all, one thing has been constant: the need to keep up with the breakneck pace of technology.

This article discusses the concept of fine-tuning in machine learning, explaining its importance for ML engineers as they strive to keep pace with emerging technologies in 2025. It highlights how different fine-tuning techniques, including reinforcement learning approaches like RLHF and new methods like Direct Preference Optimization (DPO), are redefining how models can be customized to align with human preferences and be effectively applied across various industries. The author shares personal experiences and emphasizes the necessity for ML engineers to adapt and learn these techniques to remain competitive in the evolving landscape of AI and machine learning.

Read the full blog for free on Medium.

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Published via Towards AI