Submissions for MLOps November edition
On ML workflows, tools, automation and running ML in production
On ML workflows, tools, automation and running ML in production
Krishna Gogineni
“To build or to buy?”" - That is the question which will be explored in this session.
I will compare and contrast end-to-end managed MlOps offerings like H2O.ai and sagemaker vs Building your own platform from established components vs Mixing and matching components from managed, opensource and self-built sources. As a part of this exercise, I will also cover the current state of the ecosystem in this space including the feature-richness of the managed options, maturity of the available open-source options (MlFlow, KubeFlow etc) and effort required to build your own components.
Long story short, there is truly no one-size-fits-all solution in this space, so I will also touch upon when would ROI from a certain path come out better than the alternatives and how to best take an informed decision in your context.
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