What is AI Cloud?
The AI cloud, a concept that businesses are just now beginning to use, combines cloud computing and artificial intelligence (AI). It is being driven by two factors: the delivery of new, increased value with AI tools and software to cloud computing, which is no longer just an affordable option for data storage and computation but is also playing a significant role in the adoption of AI.
To deliver AI software-as-a-service on a hybrid cloud infrastructure, the AI cloud combines AI hardware and software (including open source). This gives businesses access to AI and enables them to take advantage of AI capabilities.
AI algorithms require a lot of processing power to run, making them too costly for many businesses. However, this barrier is now gone thanks to the recent introduction of AI software-as-a-service, like software-as-a-service or infrastructure-as-a-service.
Why AI cloud
One of its most appealing benefits is that an AI cloud solves some businesses’ issues. In addition to democratizing AI and increasing its usability, an AI Cloud enables co-creation and innovation, lowers adoption costs, and propels AI-powered transformation for businesses.
The cloud’s flexibility, agility, and scale support AI-driven initiatives that provide strategic inputs for decision-making. This intelligence can be powered by the cloud in a significant way. The cloud significantly broadens the application and sphere of influence of AI, first within the user enterprise and then across the wider market. In fact, AI and the cloud will benefit from one another, enabling the cloud to realize AI’s full potential.
Workloads for AI are inherently memory and computing-intensive, whether they involve running current models or developing new ones. Large memory and processor footprints are required by workloads for video, speech, or large amounts of text data. These resources can be easily and automatically provisioned in the cloud. These AI services offer clients access to curated datasets, trained models, and a full tool stack, all to their advantage.
The infrastructure management layer, which is the lowest layer of a cloud-hosted AI platform, is crucial for ensuring that computing is cloud- and hyperscale-agnostic and scalable on-demand.
The engineering lifecycle management layer is the next one, and it is crucial in ensuring that the AI vendor and technology workbench are independent. This promotes standardization and deskilled deployment. It guarantees optimal hardware utilization and deployment that is independent of processor (CPU/GPU) architecture.
The middle layer provides operational visibility while responsibly governing AI and the digital workforce.
After that, the API layer ensures standardization or “Uberization” of technology services on demand by enabling the larger developer community to use pre-defined base models.
The experience layer, which is the topmost layer, provides access to resources, capabilities, and expertise and promotes collaboration, reusing, learning, and crowdsourcing.
The future of AI cloud
Enterprise software is currently the primary means of utilizing AI. As this software moves more of its operations to the cloud, it helps make AI clouds a reality. The main way to use AI today is through enterprise software, and as this software becomes more cloud-based, it contributes to the reality of AI cloud. The future lies in working with businesses to develop scenarios and models that are domain-specific for various industries, such as telecom, manufacturing, healthcare, finance, and insurance. By serving a specific audience with specific needs, organizations can realize their goal of becoming AI-first businesses by quickly integrating AI capabilities.
