Blog posts tagged "machine learning"

Date: 11 – 13 August 2025 Booth: 353 Book a meeting You know the old saying: what happens in Vegas… transforms your AI journey with trusted open source. On August 11-13, Canonical is back at AI4 2025 to share the secrets of building secure, scalable AI infrastructure to accelerate every stage of your machine learning

The landscape of artificial intelligence is rapidly evolving, demanding robust and scalable infrastructure. To meet these challenges, we’ve developed a comprehensive reference architecture (RA) that leverages the power of open-source tools and cutting-edge hardware. This architecture, built on Canonical’s MicroK8s and Charmed Kubeflow, ru

Welcome back, data scientists! In my previous post, we explored how easy it is to set up a machine learning environment with Canonical’s Data Science Stack (DSS) and run your first model using Hugging Face’s Smol Course. Today, let’s take it a step further with experiment tracking. Experimentation is at the heart of data science,

GenAI is transforming how we approach technology. This blog explores how you can use Canonical’s Data Science Stack (DSS) to set up your environment and dive into Hugging Face’s new self-paced course on LLMs. Learn how to build your first model and explore new GenAI topics this year!

Join Canonical Data and AI team at Data Innovation Summit 2024

Canonical announced today that Charmed MLFlow, Canonical’s distribution of the popular machine learning platform, is now generally available. Charmed MLFlow is part of Canonical’s growing MLOps portfolio.

Large language models (LLMs) are machine-learning models specialised in understanding natural language. They became famous once ChatGPT was widely adopted around the world, but they have applications beyond chatbots. LLMs are suitable to generate translations or content summaries. This blog will explain large language models (LLMs), inclu

Data scientists and machine learning engineers are often looking for tools that could ease their work. Kubeflow and MLFlow are two of the most popular open-source tools in the machine learning operations (MLOps) space. They are often considered when kickstarting a new AI/ML initiative, so comparisons between them are not surprising.  This

Canonical’s MLOps portfolio is growing with a new machine learning tool. Charmed MLFlow 2.1 is now available in Beta. MLFlow is a crucial component of the open-source MLOps ecosystem. The project announced it had passed 10 million monthly downloads at the end of 2022. With Charmed MLFlow users benefit from a platform where they can

Data pipelines are the backbone of Machine Learning projects. They are responsible for collecting, storing, and processing the data that is used to train and deploy machine learning models. Without a data pipeline, it would be very difficult to manage the large amounts of data that are required for machine learning projects. For this long

MLOps (short for machine learning operations) is slowly evolving into an independent approach to the machine learning lifecycle that includes all steps – from data gathering to governance and monitoring. It will become a standard as artificial intelligence is moving towards becoming part of everyday business, rather than an innovative act

MLOps is the short term for machine learning operations and it represents a set of practices that aim to simplify workflow processes and automate machine learning and deep learning deployments. It accomplishes the deployment and maintenance of models reliably and efficiently for production, at a large scale. MLOps is slowly evolving into

From brick-and-mortar stores to online marketplaces, retail companies are all increasing their investments in artificial intelligence, in order to gain a competitive advantage!

Google just announced that they have submitted an application for Kubeflow to become an incubating project in the Cloud Native Computing Foundation (CNCF). It is an initiative supported by the Kubeflow Project Steering group. The request is visible to everyone and it represents a game changer for the rhythm which Kubeflow will develop. It

To create a machine learning model, you need to design and optimise the model’s architecture. This involves performing hyperparameter tuning, to enable developers to maximise the performance of their work. How do hyperparameters differ from model parameters? Michal Hucko, Kubeflow engineer, and Andreea Munteanu, Product Manager will host

We are happy to announce that Charmed Kubeflow 1.6 is now available in Beta. Kubeflow has evolved into an end-to-end MLOps platform for optimised complex model training. We’re looking for data scientists, ML engineers and developers to take the Beta release for a drive and share their feedback! Read on to learn more. Read more