Blog posts tagged
"deep learning"

15 posts

Canonical
8 November 2022

Charmed Kubeflow now integrates with MindSpore

Article AI

The integration allows users to leverage deep learning for AI/ML projects within the MLOps platform On 8 November 2022, at Open Source Experience Paris, Canonical announced that Charmed Kubeflow, Canonical’s enterprise-ready Kubeflow distribution, now integrates with MindSpore, a deep learning framework open-sourced by...

Canonical
8 November 2022

aymen frikha
28 July 2021

From notebooks to pipelines with Kubeflow KALE

Article AI

What is Kubeflow? Kubeflow is the open-source machine learning toolkit on top of Kubernetes. Kubeflow translates steps in your data science workflow into Kubernetes jobs, providing the cloud-native interface for your ML libraries, frameworks, pipelines and notebooks. Read more about Kubeflow Notebooks in Kubeflow Within...

aymen frikha
28 July 2021

Rui Vasconcelos
17 May 2021

A guide to ML model serving

Article AI

TL;DR: How you deploy models into production is what separates an academic exercise from an investment in ML that is value-generating for your business. At scale, this becomes painfully complex. This guide walks you through industry best practices and methods, concluding with a practical tool, KFServing, that tackles...

Rui Vasconcelos
17 May 2021

Rui Vasconcelos
23 April 2021

What is KFServing?

Article AI

TL;DR: KFServing is a novel cloud-native multi-framework model serving tool for serverless inference. A bit of history KFServing was born as part of the Kubeflow project, a joint effort between AI/ML industry leaders to standardize machine learning operations on top of Kubernetes. It aims at solving the difficulties of...

Rui Vasconcelos
23 April 2021

Rui Vasconcelos
2 July 2020

Building Kubeflow pipelines: Data science workflows on Kubernetes – Part 2

Article AI

This blog series is part of the joint collaboration between Canonical and Manceps. Visit our AI consulting and delivery services page to know more. Introduction Kubeflow Pipelines are a great way to build portable, scalable machine learning workflows. It is a part of the Kubeflow project that aims to reduce the...

Rui Vasconcelos
2 July 2020

Rui Vasconcelos
24 June 2020

Demystifying Kubeflow pipelines: Data science workflows on Kubernetes – Part 1

Article AI

Kubeflow Pipelines are a great way to build portable, scalable machine learning workflows. It is one part of a larger Kubeflow ecosystem that aims to reduce the complexity and time involved with training and deploying machine learning models at scale. In this blog series, we demystify Kubeflow pipelines and showcase...

Rui Vasconcelos
24 June 2020

Rui Vasconcelos
26 May 2020

Kubernetes for Data Science: meet Kubeflow

Article AI

Deep Learning is set to thrive Data science has exploded as a practice in the past decade and has become an undisputed driver of innovation. The forcing factors behind the rising interest in Machine Learning, a not so new concept, have consolidated and created an unparalleled capacity for Deep Learning, a subset of...

Rui Vasconcelos
26 May 2020

James Donner
3 April 2017

Cloud Chatter: March 2017

Article Cloud and server

Our March edition is packed with exciting content. We begin with our recent announcement of Ubuntu 12.04 Extended Security Maintenance providing ongoing security updates for Ubuntu 12.04 LTS at least another year. Download our latest ‘Carrier Cloudification’ eBook, or join our upcoming webinars on OpenStack, Containers,...

James Donner
3 April 2017

Maarten Ectors
13 March 2017

This elevator catches intruders, saves lives, generates money, …

Article Internet of Things

The world is becoming software defined and most people don’t realise what this means until software apps and app stores invade their day to day objects like elevators. This blog post is about the smartest elevator demoed at MWC17 and the future of elevators with app stores. What happens if we add artificial intelligence to

Maarten Ectors
13 March 2017

Samuel Cozannet
9 March 2017

GPUs and Kubernetes for deep learning — Part 3/3: Automating Tensorflow

Article Cloud and server

Here we are. After having spent 21min reading how to build a GPU Kubernetes cluster on AWS, 7min on adding EFS storage, you want to get to the real thing, which is actually DO something with it. So today we are going to define, design, deploy and operate a Deep Learning pipeline. So what is

Samuel Cozannet
9 March 2017

Samuel Cozannet
7 March 2017

GPUs and Kubernetes for deep learning — Part 2/3: Adding storage

Article Cloud and server

Earlier this week we built a GPU cluster and installed Kubernetes so that we can do some advanced data processing. What is the thing you need next right after you have GPUs? Data. Data. and Data. And technically, if you looked at any of the tutorials for Tensorflow or the recent PaddlePaddle blog posts, you’ll

Samuel Cozannet
7 March 2017

Samuel Cozannet
15 February 2017

GPUs and Kubernetes for deep learning — Part 1/3

Article Cloud and server

A few weeks ago I shared a side project about Building a DYI GPU cluster for k8s to play with Kubernetes with a proper ROI vs. AWS g2 instances. This was spectacularly interesting when AWS was lagging behind with old nVidia K20s cards (which are not supported anymore on the latest drivers). But with the

Samuel Cozannet
15 February 2017

Adam Stokes
9 June 2016

What’s the easiest way to start using big software? Meet Conjure-up

Article Cloud and server

Have a big software project you want to get in front of users with the least amount of barriers? Maybe it has a lot of dependencies, target runtimes, and/or micro-service type relationships. Don’t feel like writing a book’s worth of install and configuration documentation? Wish you could just tell folks to just...

Adam Stokes
9 June 2016

Guest
31 May 2016

Building a nervous system for OpenStack

Article Cloud and server

Big Software is a new class of software composed of so many moving pieces that humans, by themselves, cannot design, deploy or operate them. OpenStack, Hadoop and container-based architectures are all byproducts of Big Software. The only way to address the complexity is with automatic, AI-powered analytics. Summary...

Guest
31 May 2016

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