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

Generative AI on a GPU-Instance with Ubuntu on AWS: Part 1 – Image Generation

This blog post will show you how to run one of the most used Generative AI models for Image generation on Ubuntu on a GPU-based EC2 instance on AWS

Generative AI explained

When OpenAI released ChatGPT on November 30, 2022, no one could have anticipated that the following 6 months would usher in a dizzying transformation for...

Canonical 发布 MicroCloud:人人触手可及的低接触私有云

Canonical 正式发布 MicroCloud — 一款低接触开源云解决方案。MicroCloud 是 Canonical 不断增长的云基础架构产品组合中的一员,专为所有类型企业的可扩展集群和边缘部署而构建。其设计集简单性、安全性和自动化于一身,最大限度地减少了其部署和维护的时间与精力。MicroCloud...