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
London called, and the world answered: creating a Summit without borders
When we announced that the Ubuntu Summit 25.10 would be a remote event, we knew we were taking a big step. We asked ourselves: how can we capture the spirit...
Canonical announces Ubuntu support for the NVIDIA Rubin platform
Official Ubuntu support for the NVIDIA Rubin platform, including the NVIDIA Vera Rubin NVL72 rack-scale systems, announced at CES 2026 CES 2026, Las Vegas. –...
Harnessing the potential of 5G with Kubernetes: a cloud-native telco transformation perspective
Telecommunications networks are undergoing a cloud-native revolution. 5G promises ultra-fast connectivity and real-time services, but achieving those benefits...