The latest News and Information on DevOps, CI/CD, Automation and related technologies.
Microsoft Teams has emerged as a cornerstone of modern workplace collaboration, offering a suite of tools designed to streamline communication and project management. Despite its widespread adoption, it’s crucial to acknowledge that no platform is without its limitations. Understanding these constraints is essential for organizations to navigate potential roadblocks effectively and make informed decisions about their communication infrastructure.
In a traditional DevOps implementation, you automate the build, test, release, and deploy process by setting up a CI/CD workflow that runs whenever a change is committed to a code repository. This approach is also useful in MLOps: If you make changes to your machine learning logic in your code, it can trigger your workflow. But what about changes that happen outside of your code repository?
With automation and CI/CD practices, the entire AI workflow can be run and monitored efficiently, often by a single expert. Still, running AI/ML on GPU instances has its challenges. This tutorial shows you how to meet those challenges using the control and flexibility of CircleCI runners combined with Scaleway, a powerful cloud ecosystem for building, training, and deploying applications at scale.
