Forem

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 Future of AI, LLMs, and Observability on Google Cloud

Datadog sat down with Google’s Director of AI to discuss the current and future states of AI, ML, and LLMs on Google Cloud. Discover 7 key insights for technical leaders, covering everything from upskilling teams to observability best practices

Learn More

Top comments (0)

Postmark Image

Speedy emails, satisfied customers

Are delayed transactional emails costing you user satisfaction? Postmark delivers your emails almost instantly, keeping your customers happy and connected.

Sign up