10 Top MLOps Books for Data Scientists
In today’s hype of Machine learning where many organizations have integrated or are trying to integrate ML systems into their products and platforms.
However, there are many challenges in bringing your machine learning systems into production which include construction, integrating, testing, releasing, deployment, and infrastructure management. Therefore it is important to follow good practices and know how to overcome these challenges.
MLOps is a relatively new but rapidly growing practice. The books highlighted in this article provide practical guides to understanding the practice as a machine learning project stakeholder and enable your ML workflows’ practices. These resources are divided into categories from introductory level to advanced one.
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