Submissions for MLOps November edition

On ML workflows, tools, automation and running ML in production

This project is accepting submissions for MLOps November conference edition.

The first edition of the MLOps conference was held on 23, 24 and 27 July. Details about the conference including videos and blog posts are published at https://hasgeek.com/fifthelephant/mlops-conference/

Contact information: For inquiries, contact The Fifth Elephant on fifthelephant.editorial@hasgeek.com or call 7676332020.

Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Krishna Gogineni

@krishna765

ROI of building internal MlOps vs adopting open-source vs buying managed options

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.

Comments

{{ gettext('Login to leave a comment') }}

{{ gettext('Post a comment…') }}
{{ gettext('New comment') }}
{{ formTitle }}

{{ errorMsg }}

{{ gettext('No comments posted yet') }}

Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more