How is AI/ML changing DevOps?

This post introduces the concept of applying DevOps principles to AI/ML development, emphasizing the need for a unified approach to address challenges like experiment repeatability, tool silos, and collaboration. It outlines a strategic vision for AI/ML programs, including DataOps (data acquisition and transformation) and MLOps (experimentation, training, testing, and deployment of models), and advocates for breaking down silos between data science and software development teams. The post draws parallels between the evolution of DevOps and the current state of AI/ML adoption, suggesting that learning from past DevOps successes can accelerate AI/ML initiatives.