Virtual machines (VMs) have transformed infrastructure deployment and management. VMs are so ubiquitous that I can’t think of a single instance where I deployed production code to a bare metal server in my many years as a professional software engineer. VMs provide secure, isolated environments hosting your choice of operating system while sharing the resources of the underlying server. This allows resources to be allocated more efficiently, reducing the cost of over-provisioned hardware.
In Episode 1 of the OCTOpod, Alan Clark talks with Thierry Carrez about open source communities: what they are, how they work and how you can get involved.
Building successful machine learning (ML) production systems requires a specialized re-interpretation of the traditional DevOps culture and methodologies. MLOps, short for machine learning operations, is a relatively new engineering discipline and a set of practices meant to improve the collaboration and communication between the various roles and teams that together manage the end-to-end lifecycle of machine learning projects.
