Internationally Known AI and Cloud Computing Thought Leader and Influencer, Enterprise Technology Innovator, Educator, Author, Speaker, Business Leader, Over the Hill Mountain Biker.
3 secrets to deploying LLMs on cloud platforms In the past two years, I’ve been involved with generative AI projects using large language models (LLMs) more than traditional systems. I’ve become nostalgic for serverless cloud computing. Their applications range from enhancing conversational AI to providing complex analytical solutions across industries and many functions beyond that. Many enterprises deploy these models on cloud platforms because there is a ready-made ecosystem of public cloud providers and it’s the path of least resistance. However, it’s not cheap.To read this article in full, please click here InfoWorld Cloud Computing April 16th 2024 https://buff.ly/3VVfau7 #CloudComputing #Cloud #CloudArchitecture #MultiCloud
Jo Peterson
5mo
Well done. No need to build for the Christmas 🎄 rush again 😊
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
5moDeploying LLMs on cloud platforms demands meticulous planning and execution. Optimal resource allocation, efficient scaling strategies, and robust security measures are crucial. You talked about deploying LLMs on cloud platforms; have you encountered challenges in optimizing cost-effectiveness while ensuring high performance and scalability? If you imagine a scenario where real-time language translation for low-resource languages is needed, how would you technically utilize cloud resources to deploy and maintain LLMs for such a specialized task?