Machine Learning Operations (MLOps): Deploy at Scale
Alex Cattle
on 10 September 2019
Tags: artificial intelligence , devops , Kubeflow , kubernetes , machine learning , Ubuntu
This article is more than 5 years old.
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!
Run Kubeflow anywhere, easily
With Charmed Kubeflow, deployment and operations of Kubeflow are easy for any scenario.
Charmed Kubeflow is a collection of Python operators that define integration of the apps inside Kubeflow, like
katib or pipelines-ui.
Use Kubeflow on-prem, desktop, edge, public cloud and multi-cloud.
What is Kubeflow?
Kubeflow makes deployments of Machine Learning workflows on Kubernetes simple, portable and scalable.
Kubeflow is the machine learning toolkit for Kubernetes. It extends Kubernetes ability to run independent and
configurable steps, with machine learning specific frameworks and libraries.
Install Kubeflow
The Kubeflow project is dedicated to making deployments of machine learning workflows on Kubernetes simple,
portable and scalable.
You can install Kubeflow on your workstation, local server or public cloud VM. It is easy to install
with MicroK8s on any of these environments and can be scaled to high-availability.
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