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Blog posts tagged
"artificial intelligence"

Jehudi
28 March 2024

Deploying Open Language Models on Ubuntu

AI Ubuntu tech blog

Discover the benefits of using Ubuntu for open-source AI and how to seamlessly deploy models on Azure, including leveraging GPU and Confidential Compute capabilities. ...

Carlos Bravo
27 March 2024

Generative AI with Ubuntu on AWS. Part II: Text generation

AI Ubuntu tech blog

In our previous post, we discussed how to generate Images using Stable Diffusion on AWS. In this post, we will guide you through running LLMs for text generation in your own environment with a GPU-based instance in simple steps, empowering you to create your own solutions. Text generation, a trending focus in generative AI, facilitates ...

victoriaantipova
14 March 2024

Join the Canonical Data and AI team at Data Innovation Summit 2024

AI Ubuntu tech blog

Join Canonical Data and AI team at Data Innovation Summit 2024 ...

Carlos Bravo
2 February 2024

Generative AI on a GPU-Instance with Ubuntu on AWS: Part 1 – Image Generation

Ubuntu Ubuntu tech blog

This blog post will show you how to run one of the most used Generative AI models for Image generation on Ubuntu on a GPU-based EC2 instance on AWS ...

Andreea Munteanu
16 July 2023

What does the future of AI hold in store?

AI Ubuntu tech blog

Eight trends to keep an eye on this Artificial Intelligence Appreciation Day On 16 July the world celebrates International Artificial Appreciation Day. In the previous century, science fiction often covered topics and inventions that are now closer to science fact, such as humanoid robots. In the 50s, artificial intelligence met both grea ...

Canonical
8 September 2022

Charmed Kubeflow 1.6 is now available from Canonical

AI Ubuntu tech blog

The latest release of Canonical’s end-to-end MLOps platform brings advanced AI/ML training capabilities 8 September 2022- Canonical, the publisher of Ubuntu, announces today the release of Charmed Kubeflow 1.6,  an end-to-end MLOps platform with optimised complex model training capabilities.  Charmed Kubeflow is Canonical’s enterprise-rea ...

Rob Gibbon
20 May 2021

Data Lab, Data Lake, Data Hub: what’s the difference?

AI Ubuntu tech blog

In this post we’ll explore the concepts of data lake, data hub and data lab. There are many opinions and interpretations of these concepts, and they are broadly comparable. In fact, many might say they’re synonymous and we’re just splitting hairs. Let’s look again. ...

Alex Cattle
1 April 2020

Accelerate AI/ML workloads with Kubeflow and System Architecture

AI Ubuntu tech blog

AI/ML model training is becoming more time consuming due to the increase in data needed to achieve higher accuracy levels. This is compounded by growing business expectations to frequently re-train and tune models as new data is available. The two combined is resulting in heavier compute demands for AI/ML applications. This trend is set t ...

Alex Cattle
6 February 2020

Edge AI in a 5G world

AI Ubuntu tech blog

Deploying AI/ML solutions in latency-sensitive use cases requires a new solution architecture approach for many businesses. Fast computational units (i.e. GPUs) and low-latency connections (i.e. 5G) allow for AI/ML models to be executed outside the sensors/actuators (e.g. cameras & robotic arms). This reduces costs through lower hardware ...

Alex Cattle
10 September 2019

Machine Learning Operations (MLOps): Deploy at Scale

Cloud and server Ubuntu tech blog

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 succe ...

Alex Cattle
25 July 2019

Getting started with AI

Cloud and server Ubuntu tech blog

From the smallest startups to the largest enterprises alike, organisations are using Artificial Intelligence and Machine Learning to make the best, fastest, most informed decisions to overcome their biggest business challenges. But with AI/ML complexity spanning infrastructure, operations, resources, modelling and compliance and security, ...

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