Blog posts tagged "machine learning"

Let’s meet at AI4 and talk about AI infrastructure with open source

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...

Accelerating AI with open source machine learning infrastructure

The landscape of artificial intelligence is rapidly evolving, demanding robust and scalable infrastructure. To meet these challenges, we’ve developed a...

Experiment Tracking with MLFlow in Canonical’s Data Science Stack

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...

How to build your first model using DSS

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...

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

Join Canonical Data and AI team at Data Innovation Summit 2024

Canonical releases Charmed MLFlow

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

Large language models (LLMs): what, why, how?

Large language models (LLMs) are machine-learning models specialised in understanding natural language. They became famous once ChatGPT was widely adopted...

Kubeflow vs MLFlow: which one to choose?

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...

Charmed MLFlow Beta is here. Try it out now!

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...

Four Challenges for ML data pipeline

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...

From model-centric to data-centric MLOps

MLOps (short for machine learning operations) is slowly evolving into an independent approach to the machine learning lifecycle that includes all steps – from...

What is MLOps?

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...

AI/ML in retail: how the shopping experience has changed

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

Kubeflow just applied to join CNCF – what does it mean for you?

Google just announced that they have submitted an application for Kubeflow to become an incubating project in the Cloud Native Computing Foundation (CNCF). It...

Hyperparameter tuning for ML models

To create a machine learning model, you need to design and optimise the model’s architecture. This involves performing hyperparameter tuning, to enable...

Charmed Kubeflow 1.6 Beta is out: try it today!

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...

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