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
@krishna765
Submitted Jun 30, 2021
“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.
{{ gettext('Login to leave a comment') }}
{{ gettext('Post a comment…') }}{{ errorMsg }}
{{ gettext('No comments posted yet') }}