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 it will distribute NVIDIA DOCA-OFED in Ubuntu
Today Canonical, the publishers of Ubuntu, announced that it will integrate and distribute the NVIDIA DOCA-OFED networking driver with Ubuntu.
Meet Canonical at NVIDIA GTC 2026
Previewing at the event: NVIDIA CUDA support in Ubuntu 26.04 LTS, NVIDIA Vera Rubin NVL72 architecture support in Ubuntu 26.04 LTS, Canonical’s official...
Supporting more identity providers on Ubuntu with the new Authd OIDC broker
Today we are announcing the general availability of the new generic OpenID Connect (OIDC) broker for Authd. With enterprises needing to centralise access...