A Tour of End-to-End Machine Learning Platforms
A Tour of End-to-End Machine Learning Platforms | Databaseline
“Machine Learning (ML) is known as the high-interest credit card of technical debt. It is relatively easy to get started with a model that is good enough for a particular business problem, but to make that model work in a production environment that scales and can deal with messy, changing data semantics and relationships, and evolving schemas in an automated and reliable fashion, that is another matter altogether. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!
As little as 5% of the actual code for machine learning production systems is the model itself. What turns a collection of machine learning solutions into an end-to-end machine learning platform is an architecture that embraces technologies designed to speed up modelling, automate the deployment, and ensure scalability and reliability in production. I talked about lean D/MLOps, data and machine learning operations, before, because machine learning operations without data is pointless, so an end-to-end machine learning platform needs a holistic approach…”