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

Kubernetes on a single machine

As developers, we do not always have access to a production-like environment to test new features and run proof-of-concepts. This is why it can be very...

Ubuntu 19.10 delivers Kubernetes at the edge, multi-cloud infrastructure economics and an integrated AI/ML developer experience

17th October 2019: Canonical today announced the release of Ubuntu 19.10 with a focus on accelerating developer productivity in AI/ML, new edge capabilities...

Ansible vs Terraform vs Juju: Fight or cooperation?

Ansible vs Terraform vs Juju vs Chef vs SaltStack vs Puppet vs CloudFormation – there are so many tools available out there. What are these tools? Do I need...