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


ilvipero
22 June 2026

Ubuntu Summit 26.04: connected by open source

Ubuntu Article

What an incredible experience! Ubuntu Summit 26.04 has officially drawn to a close, but the energy from our global community is still buzzing – in the comments section, on social media, and in news reports. Whether you joined us in person or tuned in from across the globe, you helped make this edition our most ...


Pedro Lazzarotto
12 June 2026

A decade of Ubuntu on IBM Z and IBM LinuxONE

Partners Ubuntu tech blog

This year we celebrate a decade of Ubuntu Server support on the s390x architecture: marking a long-standing collaboration between Canonical and IBM that began at LinuxCon 2015. The first release happened on April 21, 2016, bringing Ubuntu 16.04 LTS (Xenial Xerus) to IBM Z and IBM LinuxONE platforms.  A first for Ubuntu on IBM That ...


Hugo Huang
28 May 2026

Canonical announces optimized Ubuntu images for TPU virtual machines by Google Cloud

AI Ubuntu tech blog

Canonical and Google Cloud announced the availability of certified Ubuntu images for Google’s Cloud TPU Virtual Machines. ...