Machine Learning Operations (MLOps): Deploy at Scale
Alex Cattle
on 10 September 2019
Tags: artificial intelligence , devops , Kubeflow , kubernetes , machine learning , Ubuntu

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!
Enterprise AI, simplified
AI doesn’t have to be difficult. Accelerate innovation with an end-to-end stack that delivers all the open source tooling you need for the entire AI/ML lifecycle.
Newsletter signup
Related posts
Canonical announces optimized Ubuntu images for TPU virtual machines by Google Cloud
Canonical and Google Cloud announced the availability of certified Ubuntu images for Google’s Cloud TPU Virtual Machines.
Introducing Workshop: launch sandboxed development environments on Ubuntu with a single command
Developers now benefit from consistency and repeatability for cutting-edge workflows, including agentic AI. Today, Canonical announced the release of...
Run agentic workloads on Arm and Ubuntu
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...