MLOps [7] - What parts of an ML project should be in source control?
With traditional software development, it's important that everything goes in source control. The same can be said for ML projects, but there are some important considerations! »
MLOps [6] - How does MLOps differ from DevOps?
While machine learning projects and traditional software development have many similarities, there are some important differences. This means blindly applying the practices of one to the other is unlikely to be successful. »
MLOps [5] - What DevOps practices can be applied to Machine Learning projects?
There are many lessons and good practices we've learned from decades of software development and more than a decade of "DevOps". Most (but not all) of these can be applied to machine learning projects. »
MLOps [4] - What is AIOps and how does it differ from MLOps?
MLOps and AIOps both sit at the union of DevOps and AI. They may sound like the same thing, but they represent completely different ideas. So what is AIOps? »
MLOps [3] - What is MLOps?
MLOps sounds like just another buzzword, but it presents some important concepts and practices for maturing your machine learning projects. »