“I believe the reason we have grown so much is the scalability and extensibility of Kubernetes i.e. give us an [Internet of Things] edge device, give us a traditional server or any other computing entity and we can see how cloud-native Kubernetes is fit for purpose, at scale. As we now stand and see that the world of AI has impacted the technology universe, it’s not hard to see why people have referred to this period as the ‘age of irrational exuberance’ – and we can remember that the term itself came from ex-chairman of the Federal Reserve Alan Greenspan. But as we look back through the ages from steam, to early train travel, through automotive innovation and manufacturing, nothing important has ever really been innovated without a degree of irrational exuberance being involved, often where people have considered initial innovations in any given area to be outlandish or misguided at first.” – Priyanka Sharma, CNCF Executive Director, May 2024
CNCF published its guidance on cloud native artificial intelligence (CNAI). The white paper discusses what CNAI is and the opportunities it presents. It also details challenges associated with CNAI.
CNAI is an approach to building and deploying AI applications using cloud native principles. This allows AI practitioners to focus on their domain and reduces toil. There are various challenges associated with CNAI, including data preparation, model training, model serving, user experience, and cross-cutting concerns.Combining Artificial Intelligence (AI) and Cloud Native (CN) 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. Additionally, ongoing research into GPU scheduling, vector databases, and sustainability offers promising solutions for overcoming limitations. As AI and Cloud Native technologies mature, organizations embracing this synergy will be well positioned to unlock significant competitive advantages. The possibilities are endless, from automating complex tasks and analyzing vast datasets to generating creative content and personalizing user experiences. 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.
https://www.cncf.io/wp-content/uploads/2024/03/cloud_native_ai24_031424a-2.pdf