Artificial intelligence (AI) and machine learning (ML) have disrupted industries worldwide, creating a cascade of innovation and a seismic shift in how organizations conduct operations. Meta's whopping $40 billion investment in AI research underpins the scale of change on the horizon. These AI-driven initiatives of such magnitude need robust infrastructure, agility, and streamlined operations. This is where DevOps comes into play.DevOps, a mix of 'development' and 'operations,' is an organizational approach that enables swift development, testing, and delivery of software. By integrating developers and operations teams, DevOps enhances communication, reduces bottlenecks, and accelerates time-to-market. For AI initiatives, particularly, DevOps brings scalability, consistency, and accelerated development and deployment of AI models.Best Practices in DevOps for AI1. Continuous Integration and Continuous Delivery (CI/CD): This practice involves routinely integrating code changes to a central repository, followed by automated testing and deployment. In the context of AI/ML, this includes integrating new data, retraining models, and deploying models in production environments.2. Version Control: Tracking and management of changes to code, configurations, and data enable teams to roll back and forward any changes while maintaining consistency. 3. Monitoring and Logging: Keeping an eye on model performance and system logs helps in identifying and resolving anomalies while ensuring system performance.Embedded within Meta's colossal investment is the backbone of a robust DevOps strategy. Great strides have been made to deliver high-quality software quickly and reliably through DevOps. Looking forward, the evolving DevOps landscape, alongside burgeoning AI initiatives, is set to reshape the technology industry, perhaps on a scale far greater than we anticipate.