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. »
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. »
Earlier this week, I was invited to speak about DevOps at the University of Queensland Computing Society. Regardless of your experience, if you're new to DevOps, this recording is a good place to start. »
DevOps is full of great practices and patterns, and most of these can be applied to machine learning and data science projects. »
In my last post I gave you two suggestions for how to find out what to type when creating an Azure Pipelines YAML Build. Here are a few more! »