Unpacking the Differences: Machine Learning in Research versus Production Environments
Last Updated on April 6, 2023 by Editorial Team
Author(s): Youssef Hosni
Originally published on Towards AI.
Machine Learning has become a buzzword in the tech industry and beyond, with its applications ranging from image recognition to natural language processing. However, the way machine learning is implemented varies greatly depending on the environment in which it is used.
In this article, we will explore the differences between machine learning in research environments versus production environments. We will delve into the components of production machine learning and the challenges it poses to provide a better understanding of the complexities involved.
Photo by Kseniia Samoylenko on UnsplashMachine Learning in Research vs. Machine Learning in ProductionComponents of Production Machine LearningChallenges in Production… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Take our 90+ lesson From Beginner to Advanced LLM Developer Certification: From choosing a project to deploying a working product this is the most comprehensive and practical LLM course out there!
Towards AI has published Building LLMs for Production—our 470+ page guide to mastering LLMs with practical projects and expert insights!

Discover Your Dream AI Career at Towards AI Jobs
Towards AI has built a jobs board tailored specifically to Machine Learning and Data Science Jobs and Skills. Our software searches for live AI jobs each hour, labels and categorises them and makes them easily searchable. Explore over 40,000 live jobs today with Towards AI Jobs!
Note: Content contains the views of the contributing authors and not Towards AI.