Machine Learning Operations (MLOps): Deploy at Scale

Alex Cattle

on September 10, 2019

What do successful deployments have in common?

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

Newsletter signup

Select topics you’re
interested in

In submitting this form, I confirm that I have read and agree to Canonical’s Privacy Notice and Privacy Policy.

Related posts

We are changing the way you build snaps from GitHub repos

On the 11th March 2020 we introduced a new process for building a snap using GitHub repos to snapcraft.io. Here is all you need to know about this update....

GNOME 3.34 snapcraft extension

We constantly strive to empower developers. Part of that aim extends to making development easier, for example improving build tools and documentation. As an...

An adventure through the Snap Store

An application store with a large number of entries is a double-edged sword. It’s often a good sign of a vibrant, thriving community of software creators,...