Skip to main content
  1. Blog
  2. Article

Alex Cattle
on 10 September 2019


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

Related posts


Canonical
27 May 2026

Introducing Workshop: launch sandboxed development environments on Ubuntu with a single command

Canonical announcements Article

Developers now benefit from consistency and repeatability for cutting-edge workflows, including agentic AI. Today, Canonical announced the release of Workshop, a solution for launching development environments with a single command. These environments are configured once, and can be reproduced on different machines. This means consistent ...


Youssef Eltoukhy
26 May 2026

Run agentic workloads on Arm and Ubuntu

AI Article

In the lead-up to Ubuntu Summit 26.04, Canonical and Arm are collaborating to certify the new Arm AGI CPU on Ubuntu 26.04 LTS (Resolute Raccoon). Learn what this means for developers and agentic AI. ...


Abdelrahman Hosny
21 May 2026

Developing web apps with local LLM inference

AI Article

I’ve yet to meet a developer that enjoys working with metered AI APIs. The need to pay for every API call in development works in direct opposition to the ethos of rapid iteration, and it’s easy for the costs to get out of hand. That’s why Canonical has created a different approach to building AI-powered ...