Author: Jennifer Wales

In the last few years, technology has advanced rapidly, especially in the field of artificial intelligence. The introduction of several Generative AI tools has already started to disrupt many well-established industries, including writing, designing, customer service, etc. These generative AI tools, for example, ChatGPT, DallE-2, and Beatoven, can generate text, music, and images with just a single command.

With Generative AI by their side, developers can easily generate codes for their software projects, generate design documents, and even build the entire software application without the need to write a single piece of code. Sounds interesting?

MLOps, or Machine Learning Operations, is a set of techniques and tools for deploying models in production environments. Lately, the effectiveness of DevOps in reducing the time between software updates and eliminating gaps has been crucial to the existence of any business.

Machine learning professionals turned to the machine learning sector to implement the DevOps principle, creating MLOps. Integrating the CI/CD principle with the machine learning model enables the data world to integrate and deliver production-ready models promptly. In addition, MLOps introduce new Continuous Training (CT) and Continuous Monitoring (CM) principles, making the production environment even more suitable for any machine learning model.