🌟 Join the AI Summer Camp 🌟 Level up your #AI skills this summer with our exclusive 5-part training series brought to you by Canonical experts from August 5-9. In our fifth and final episode, we dive into the world of 𝐀𝐈 𝐚𝐧𝐝 𝐩𝐫𝐢𝐯𝐚𝐭𝐞 𝐜𝐥𝐨𝐮𝐝𝐬, where you can learn about: - Why consider a private cloud for AI? - Key considerations when building a private cloud for AI projects - Performance acceleration options for private cloud - Guidance for Kubernetes on OpenStack for AI initiatives --------- Canonical's AI Summer Camp includes: 1️⃣ How to Build LLMs with Open Source? (https://lnkd.in/dKETtH8d) 2️⃣ Kubeflow VS MLflow (https://lnkd.in/d3eCF3Nm) 3️⃣ Vector Databases for Generative AI Applications (https://lnkd.in/d9MpmSB5) 4️⃣ AI on Public Cloud with Open Source (https://lnkd.in/d4U3ZWqv) > 5️⃣ 𝐀𝐈 𝐨𝐧 𝐏𝐫𝐢𝐯𝐚𝐭𝐞 𝐂𝐥𝐨𝐮𝐝 - 𝐖𝐡𝐲 𝐢𝐬 𝐢𝐭 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐇𝐲𝐩𝐞𝐫𝐬𝐜𝐚𝐥𝐞𝐫𝐬 𝐄𝐫𝐚? Join us for the AI Summer Camp and make part of your holiday a learning opportunity. #AISummerCamp #Cloud #OpenSource #Canonical
AI Summer Camp | Episode 5: AI on private cloud in the hyperscalers era
www.linkedin.com
Canonical
3w
Welcome to the final episode of AI Summer Camp ☀ Thank you for joining us today!
Private Cloud is much complex to put in place where you need to setup (k8s, lb, api gateway...) by yourself 🧐
Hi, Munich is live
well, use cases we have more than enough, the most crucial topic is to find ML Engineers
competences (expert ressources) is the foundation 😎
Hi,from Ethiopia
Arun Kumar
3w
could you please share a reference architecture for hybrid setup for mlops?
Software Engineer II
3wCan you talk about what goes into decision making when allocating workloads to the private cloud, vs the public cloud, in a hybrid scenario?