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Making MLOps easy for End-Users
A tutorial on simplifying MLOps using open source tools
Figuring out what people mean when they say “MLOps” is hard. Figuring out how to properly do MLOps, even for a technical person, is perhaps even more difficult. How difficult must doing MLOps be, then, for a citizen data scientist that knows nothing of web technologies, Kubernetes, monitoring, cloud infrastructure, etc.? Here I continue exploring how to set up an open-source MLOps framework for this purpose: specifically, I outline and show how a combination of Databricks, mlflow, and BentoML can potentially provide a compelling, extensible, and easy-to-use MLOps workflow for end-users.
I have previously discussed that an MLOps framework must come with batteries included and support an extensive list of features; model lifecycle management and governance, monitoring, A/B testing, interpretability, drift/outlier detection, and so on. However, as the end-user:
I just want to define a given python interface, push a big green button, and get back a REST endpoint URL where I can interact with my deployed model.
In the first part of this series of blog posts, I explored how Databricks in combination with Seldon-core checks off most of what I see as requirements for deploying and running MLOps; however, the open-source offering…
