How is AI/ML changing DevOps?

What this post added

This post draws parallels between the evolution of DevOps and the current challenges in adopting AI/ML. It highlights the need for a similar integrated approach to overcome silos, improve repeatability, and foster collaboration in AI/ML projects. It introduces the concepts of DataOps (for data acquisition and transformation) and MLOps (for experimentation, training, and deployment of models) as foundational stages for leveraging AI/ML for business use cases. The post advocates for learning from the DevOps journey to build intentional processes and tools that enable efficient data handling and model deployment, ultimately driving business value.