DevOps for Machine Learning and AI
devops.com, Wednesday, July 24th, 2024
In today's technology landscape, DevOps has become synonymous with streamlined development and operations processes. However, when it comes to machine learning (ML) and artificial intelligence (AI), traditional DevOps practices face unique challenges.
The emergence of DevOps for machine learning, often referred to as MLOps, provides the framework to bridge the gap between data science, operations and innovative AI applications. It enables organizations to efficiently develop, deploy and manage ML and AI models, fostering a seamless integration of data-driven intelligence into their operational workflows.
Challenges in ML and AI Operations
Developing and deploying ML and AI models introduces complexities that challenge traditional DevOps methodologies: