Your submission was sent successfully! Close

You have successfully unsubscribed! Close

Thank you for signing up for our newsletter!
In these regular emails you will find the latest updates about Ubuntu and upcoming events where you can meet our team.Close

Machine Learning Operations (MLOps): Deploy at Scale

Alex Cattle

on 10 September 2019

This article is more than 4 years old.


Artificial Intelligence and Machine Learning adoption in the enterprise is exploding from Silicon Valley to Wall Street with diverse use cases ranging from the analysis of customer behaviour and purchase cycles to diagnosing medical conditions.

Following on from our webinar ‘Getting started with AI’, this webinar will dive into what success looks like when deploying machine learning models, including training, at scale. The key topics are:

  • Automatic Workflow Orchestration
  • ML Pipeline development
  • Kubernetes / Kubeflow Integration
  • On-device Machine Learning, Edge Inference and Model Federation
  • On-prem to cloud, on-demand extensibility
  • Scale-out model serving and inference

This webinar will detail recent advancements in these areas alongside providing actionable insights for viewers to apply to their AI/ML efforts!

Watch the webinar

kubeflow logo

Run Kubeflow anywhere, easily

With Charmed Kubeflow, deployment and operations of Kubeflow are easy for any scenario.

Charmed Kubeflow is a collection of Python operators that define integration of the apps inside Kubeflow, like katib or pipelines-ui.

Use Kubeflow on-prem, desktop, edge, public cloud and multi-cloud.

Learn more about Charmed Kubeflow ›

kubeflow logo

What is Kubeflow?

Kubeflow makes deployments of Machine Learning workflows on Kubernetes simple, portable and scalable.

Kubeflow is the machine learning toolkit for Kubernetes. It extends Kubernetes ability to run independent and configurable steps, with machine learning specific frameworks and libraries.

Learn more about Kubeflow ›

kubeflow logo

Install Kubeflow

The Kubeflow project is dedicated to making deployments of machine learning workflows on Kubernetes simple, portable and scalable.

You can install Kubeflow on your workstation, local server or public cloud VM. It is easy to install with MicroK8s on any of these environments and can be scaled to high-availability.

Install Kubeflow ›

Newsletter signup

Get the latest Ubuntu news and updates in your inbox.

By submitting this form, I confirm that I have read and agree to Canonical's Privacy Policy.

Related posts

Canonical Delivers Secure, Compliant Cloud Solutions for Google Distributed Cloud

Today, Canonical is thrilled to announce our expanded collaboration with Google Cloud to provide Ubuntu images for Google Distributed Cloud. This partnership...

Deploying Open Language Models on Ubuntu

Discover the benefits of using Ubuntu for open-source AI and how to seamlessly deploy models on Azure, including leveraging GPU and Confidential Compute capabilities.

Join the Canonical Data and AI team at Data Innovation Summit 2024

Join Canonical Data and AI team at Data Innovation Summit 2024