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.