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 technologies supporting AI development and usage, AI can enhance cloud native in many ways – from anticipating load and better resource scheduling, particularly with multiple optimization criteria involved, such as power conservation, increased resource utilization, reducing latency, honoring priorities, enhancing security, understanding logs and traces, 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.

Combining AI and cloud native technologies offers an excellent opportunity for organizations to develop unprecedented capabilities. With the scalability, resilience, and ease of use of cloud native infrastructure, AI models can be trained and deployed more efficiently and at a grander scale. This white paper delves into the intersection of these two areas, discussing the current state of play, the challenges, the opportunities, and potential solutions for organizations 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.

Depending on the reader’s background and interest, this whitepaper can be read in many ways. Exposure to microservices and cloud native technologies including Kubernetes is assumed. For those without experience in engineering AI systems, it is recommended to read from start to finish. For those further along in their AI/ML adoption or delivery journey, per their user persona, it is suggested to dive into the sections pertinent to current individual challenges. 

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.