We recently wrote an essay for O’Reilly Radar about the machine learning deployment stack and why data makes software different. The questions we sought to answer were: Why does ML need special treatment in the first place? Can’t we just fold it into existing DevOps best practices? What does a modern technology stack for streamlined ML processes look like? How can you start applying the stack in practice today?
As the industry is working to develop shared foundations, standards, and a software stack for building and deploying production-grade machine learning software and applications, we are witness to a growing gap between data scientists who create machine learning models and DevOps tools and processes to put those models into production. This often results in data scientists building machine learning pipelines that aren’t ready to be deployed and/or that are challenging to experiment and iterate with. It can also result in models that are neither properly validated nor isolated from relevant business logic.