Safety is a key element in navigating AI within the automotive ecosystem. 🚗 Listen on Spotify about the future implications of AI in automotive, as discussed by Bertrand Boisseau, our automotive industry lead at Canonical.

The emergence of DevOps has changed the way enterprises handle software delivery processes, leading to faster and improved quality. After DevOps has been coined, other practices such as DataOps, MLOps, and AIOps have emerged. In the podcast, Michelle and Andreea, Data PM and AI Product Managers, respectively, will be discussing the significance of these Ops processes in streamlining and optimizing enterprise data, machine learning, and AI projects and use cases.

This video will teach you how you can launch a Ubuntu instance on Google Vertex AI, which embodies the AI/ML tools you need for training LLM.

AI is at the heart of a revolution in the technology space. Organisations from all industries are looking for ways to put AI to work. Once they have finalised use case assessment, their next question is typically related to the environment they will use to develop and deploy their AI initiatives. They often prefer the public clouds as an initial environment, because of the computing power and ability to scale as projects mature. In addition to the infrastructure, enterprises need software where they can develop and deploy the machine learning models.

Weka report from 2024 showed that 47% of respondents will use the public cloud as the primary place to develop their machine learning projects. This is a result of a correlation of factors which include the need for compute power, easy scalability, and the ability to utilise existing infrastructure already in place on both hybrid clouds and public clouds. Join us to talk more about AI on the public cloud: what are the main benefits and what are the best practices an organisation could implement in order to easier adopt AI and leverage the most the public clouds.

Introducing Charmed MongoDB – Canonical’s enterprise-grade MongoDB database offering. Charmed MongoDB simplifies the operations of MongoDB applications through automation, security, scalability, availability and monitoring. Charmed MongoDB is the cost-effective, reliable, secure and scalable way to use MongoDB on any cloud, hybrid cloud or on-premise. It also provides additional support, managed services, and expert services, so enterprises can run MongoDB in production at a lower cost, bug-free and in the most optimised way.

AI models run on large amounts of good quality data, and when it comes to sensitive tasks like medical diagnosis or financial risk assessments, you need access to private data during both training and inference. When performing machine learning tasks in the cloud, enterprises are understandably concerned about data privacy as well as their model’s intellectual property. Additionally, stringent industry regulations often prohibit the sharing of such data.

It's the grand reveal of Ubuntu 24.04 Noble Numbat 👑 The numbat, a small enigmatic marsupial from Australia may not be the first creature that comes to mind when one ponders nobility. However, looks can be deceiving.

Curious how to start with Charmed MongoDB?

🚀 AI is reshaping our world, from entertainment to space exploration. Canonical simplifies your AI journey with long-term support and easy-to-use open-source tools. Dive into building, scaling and optimizing tools like Kubelow, MLflow, Kafka, and Spark with Canonical Data & AI solutions.