Cloud Native Artificial Intelligence

CHECKOUT: Cloud Native Artificial Intelligence

Cloud Native (CN) and Artificial Intelligence (AI) are the most critical technology trends today. Cloud Native1 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 CN technologies readily support certain aspects of AI/ML workloads, challenges and gaps remain, presenting opportunities to innovate and better accommodate.

This paper presents a brief overview of the state-of-the-art AI/ML techniques, followed by what CN technologies offer, covering the next challenges and gaps before discussing evolving solutions. The paper will equip engineers and business personnel with the knowledge to understand the changing Cloud Native Artificial Intelligence (CNAI) ecosystem and its opportunities.

What is Cloud Native Artificial Intelligence?

Cloud Native Artificial Intelligence allows the construction of practical systems to deploy, run, and scale AI workloads. CNAI solutions address challenges AI application scientists, developers, and deployers face in developing, deploying, running, scaling, and monitoring AI workloads on cloud infrastructure. By leveraging the underlying cloud infrastructure’s computing (e.g., CPUs and GPUs), network, and storage capabilities, as well as providing isolation and controlled sharing mechanisms, it accelerates AI application performance and reduces costs.

Cloud Native Artificial Intelligence (CNAI) refers to approaches and patterns for building and deploying AI applications and workloads using the principles of Cloud Native. Enabling repeatable and scalable AI-focused workflows allows AI practitioners to focus on their domain.

CHALLENGES FOR CLOUD NATIVE ARTIFICIAL INTELLIGENCE