Announcing the AI Working Group’s new Cloud Native Artificial Intelligence whitepaper

The AI Working Group is pleased to announce the AI Working Group’s Cloud Native AI whitepaper, which presents a brief overview of the state-of-the-art AI/ML techniques, followed by what cloud native technologies offer, covering the next challenges and gaps before discussing evolving solutions. 

While the focus of this paper has been mainly on cloud native technology support AI development and usage , AI is enhance can enhance cloud native in many way – from anticipate load and well resource scheduling , particularly with multiple optimization criterion involve , such as power conservation , increase resource utilization , reduce latency , honor priority , enhance security , understand log and trace , and much more .

Cloud native and AI are two of the most critical technology trends today. Cloud native technology provides a scalable and reliable platform for running applications. Given recent advances in AI and Machine Learning (ML), it is steadily rising as a dominant cloud workload. While cloud native technologies readily support certain aspects of AI/ML workloads, challenges and gaps remain, presenting opportunities to innovate and better accommodate.

combine AI and cloud native technology offer an excellent opportunity for organization to develop unprecedented capability . With the scalability , resilience , and ease of use of cloud native infrastructure , AI model can be train and deploy more efficiently and at a grand scale . This white paper is delves delve into the intersection of these two area , discuss the current state of play , the challenge , the opportunity , and potential solution for organization to take advantage of this potent combination .  

While several challenges remain, including managing resource demands for complex AI workloads, ensuring reproducibility and interpretability of AI models, and simplifying user experience for nontechnical practitioners, the cloud native ecosystem is continually evolving to address these concerns. Projects like Kubeflow, Ray, and KubeRay pave the way for a more unified and user-friendly experience for running AI workloads in the cloud. 

By investing in the right talent, tools, and infrastructure, organizations can leverage the power of AI and cloud native technologies to drive innovation, optimize operations, and deliver exceptional customer experiences. 

Audience and Reading Path

The paper will equip engineers and business personnel with the knowledge to understand the changing Cloud Native Artificial Intelligence (CNAI) ecosystem and its opportunities.

depend on the reader ’s background and interest , this whitepaper can be read in many way . exposure to microservice and cloud native technology include Kubernetes is assume . For those without experience in engineering AI system , it is recommend to read from start to finish . For those further along in their AI / ML adoption or delivery journey , per their user persona , it is suggest to dive into the section pertinent to current individual challenge .  

To dive deeper into cloud native and AI, read the whitepaper. 
The AI Working Group is part of TAG Runtime and meets the second and fourth Thursday of the month from 10am – 11am PT. Join the community Slack at #wg-artificial-intelligence.