Canonical

Join us on October 17th to find out how you can streamline the management capabilities required to support a distributed, remote workforce, utilizing Ubuntu on Amazon WorkSpaces.

The evolution of the AI landscape is accelerated by all the interest that organisations have in adopting it. However, once you look beyond the noise that media makes around the topic, it often feels that organisations are still challenged to move beyond experimentation. 📔 Scope your project before you even get started. Doing AI just for the sake of it will likely bring no ROI. Any project should solve a problem. 💻 Get started quickly with open-source tooling and fail fast. Not all projects will be suitable to go beyond experimentation and the sooner you realise it, the better it is. Simple tools such as Jupyter Notebook or Data Science Stack are enough to enable you to get started 🪈 Build pipelines to automate your ML workloads. Doing a lot of manual work for automating manual tasks defeats the initial purpose. Once you are confident in your work, building pipelines with tools such as Kubeflow Pipelines, SageMaker or VertexAI will accelerate your development process. 🎥 Do not forget about the edge(s). Almost all models that are production-ready end up being deployed to devices of different types. Having that part of your overall AI strategy will help you move beyond experimentation. It would help if you considered how you package your model to be secure and how you run it on different devices. World Summit AI, Data & AI Masters organised by Canonical and GITEX GLOBAL Largest Tech & Startup Show in the World made me reflect on these topics. The industry is moving fast, but MLOps, AI at the Edge and wider adoption in different industries are topics that are likely to come back more often in the upcoming months.

We’ve all heard the anecdotes of data scientists spending more time on tooling than ML models. There is no smoke without fire, right? The truth is AI/ML practitioners struggle with their environments for various reasons, including tooling fragmentation, package dependencies and access to computing power. Data Science Stack (DSS) is an out-of-the-box solution for data scientists and machine learning engineers, published by Canonical. It is a ready-made environment for ML enthusiasts that enables them to develop and optimize models without spending time on the necessary underlying tooling. It is designed to run on any AI workstation that runs Ubuntu, maximizing the GPU’s capability and simplifying its usage. 🔗 https://lnkd.in/dP2WCucU Join us for this webinar to learn more about data science tools, with a focus on DSS and its capabilities. During the webinar, Michal Hucko, MLOps engineer at Canonical and Andreea Munteanu, AI & MLOps Product Manager, will talk about: 🔸 Key considerations when getting started with data science 🔸Data science through the open source lens 🔸Deep dive into Data science stack (DSS) 🔸Demo of the DSS Prepare your questions and join us live to get insights into how DSS improves the developer experience for data science and ML projects on Ubuntu. 🔗 https://lnkd.in/dP2WCucU #Ubuntu #AI #DataScience #MLOps #OpenSource

What is the Zero Trust model in Cybersecurity? 🛡️ Watch the video as Ijlal Loutfi, Product Manager at Canonical, explains what Zero Trust is and why confidential computing is essential to safeguard systems against cyber attacks and vulnerabilities. Ijlal also discusses the technology that allows AI workloads to run in a confidential way. Stay tuned to our social media pages as we share educational materials to help your organizations stay secure this #CybersecurityAwareness Month. Learn how Canonical secures your open source stack here: https://lnkd.in/g4bY2H7V

Open-source tools offer greater flexibility and security for edge AI deployments. When combined with an accelerated computing stack, these solutions enable professionals to deliver projects faster, reduce operational costs, and achieve more predictable results. Canonical, Lenovo, and NVIDIA have teamed up to deliver a validated reference architecture for edge AI, that empowers developers and researchers to deliver projects faster, reduce costs, and ensure predictable outcomes. Read our blog to learn more and get access to our step-by-step guide for setting up the hardware and software necessary to execute ML workloads: https://lnkd.in/dDuda27U

Meet Brianna Stuart, Senior Customer Success Manager at Canonical. ✨ Brianna is one of the backbones behind the growth of our Customer Success team. Since the start of her career, she has made empathy the core of everything she does, and believes that is required to excel in Customer Success. Through regular meetings with customers, she is one of their early touchpoints in Canonical. “Being closely involved with my customers and getting to know them is the key to helping them succeed.” Fun fact: Brianna was once a scenic artist before she found her way to Canonical. 🎨 Want to be part of our team? Find your role fit here: https://lnkd.in/gCaMfinw #LifeatCanonical #CanonicalSpotlight #CustomerSuccess

Congratulations to AMD on the launch of the 5th Gen EPYC processors! 🎉 The latest AMD EPYC "Turin" processors offer high core density, energy efficiency, and enhanced performance. 🚀 Check out the Ubuntu Turin Tuning Guide for the best performance tips and tricks⚡ https://lnkd.in/dGj7W4bq

In the first episode of our 3-part podcast series for #CybersecurityAwareness Month, Luci Stanescu chats with Alex Murray about how cybersecurity has evolved since the early days of the internet and discusses past and recent examples of vulnerabilities. 🎙️ What other watershed moments do you feel have impacted the security landscape? Tune in to the episode on the Ubuntu Security Podcast: https://lnkd.in/dzHMTwQ2 #Cybersecurity #Opensource

Take flight with Ubuntu 24.10 #OracularOriole! Swipe through to learn about the new features that come packed with the newset #Ubuntu release. Learn more: https://lnkd.in/dmn--K-i