Machine Learning Operations (MLOps): Deploy at Scale

Alex Cattle

on 10 September 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

Ubuntu cloud

Ubuntu offers all the training, software infrastructure, tools, services and support you need for your public and private clouds.

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

5 key steps to take your IoT device to market

IoT businesses are notoriously difficult to get off the ground. No matter how good your product is or how good your team is, some of the biggest problems you...

Kubernetes: a secure, flexible and automated edge for IoT developers

Cloud native software such as containers and Kubernetes and IoT/edge are playing a prominent role in the digital transformation of enterprise organisations....

Infrastructure-as-Code mistakes and how to avoid them

Two industry trends point to a gap in DevOps tooling chosen by many. Operations teams need more than an Infrastructure-as-Code approach, but a complete...