DEV Community

Build Amazing for Egen

Posted on • Originally published at egen.solutions

2

Handle MLOps across multiple cloud providers using Kubeflow

Machine Learning Models are relatively easy to build but hard to roll out. Learn how to make ML workflows production-ready with Kubeflow.

Cloud Collective is a meetup for technologists interested in the Cloud Native approach towards developing and deploying web & mobile applications, streaming analytics, ETL pipelines, microservices, containers, functions, and applying Infrastructure as Code principles for automating infrastructure on AWS, Azure, and Google Cloud.

With the recent huge demand and traction of DevOps and GitOps, many organizations are struggling to apply those practices to ML workloads. That's the exact use case we wanted to talk about in this meetup.

Quickly Deploy ML Workloads on Multi-Cloud Using Kubeflow. Learn how to quickly produce an end-to-end AI pipeline and easily deploy ML workloads onto multi-cloud using the well-known open-source platform called Kubeflow Pipelines. All major components of the AI pipeline such as data pre-processing, hyperparameter tuning, model training, model prediction, model explanation, and training orchestration can be easily implemented on the cloud with just a few easy steps.

If you enjoyed this talk, join our cloud collective group here ► https://www.meetup.com/cloud-collective/ and participate in our next meetup!

Image of Datadog

The Essential Toolkit for Front-end Developers

Take a user-centric approach to front-end monitoring that evolves alongside increasingly complex frameworks and single-page applications.

Get The Kit

Top comments (0)

Sentry image

See why 4M developers consider Sentry, “not bad.”

Fixing code doesn’t have to be the worst part of your day. Learn how Sentry can help.

Learn more

👋 Kindness is contagious

Please leave a ❤️ or a friendly comment on this post if you found it helpful!

Okay