AI-powered technologies have initiated the transformation of the industrial landscape on a large scale. Not only has this provided organizations with tremendous benefits, but it has also added value to their businesses.
Supporting AI profoundly in its own way, the cloud architecture has brought in a transition in the way services are delivered across the requirements distributed globally. Had it not been for the cloud, AI would still have been a complex, expensive and distant technology that was far from serving the general masses. However, today, this combination has a profound effect on how everyday services and mass usage can be driven through mobile and IoT paradigms. Self-service, more human-like interfaces and interactions, and customer experience-centric applications have all been made possible due to this combination feeding each other with data and insights.
Artificial intelligence, on its part, is expected to play a part in making the hybrid-cloud orchestration intelligent by means of right workload fitment, cost analysis, real-time decision making and policy optimization to make sure IT teams can focus on the most critical tasks rather than maintaining or only provisioning systems. The same can also be expected in the case of making the hybrid cloud more compliant with the changing regulations and the regular monitoring of both active and passive artificial intelligence services.
There are AI-based platforms and AI technology services wherein AI is utilized to make the service better, predictable, and scalable. AI-as-a-service can be utilized to gain insights without investing in sophisticated infrastructure and systems. There is a lot of investment from Google, IBM, Microsoft, and AWS to create and deliver AI-as-a-service. Organizations are also developing AI-based services. For instance, Quantifi is using AI/ML to place and optimize digital advertisements and brand building. Edtech is using AI-based technology to give personalized content and attention to students in virtual mode. IBM Watson is a unique offering in this regard that can offer AI-based services as well as use AI to better its services using the cloud as a distribution model.
Essentials of Consuming AI in Hybrid Cloud World
Currently, for the development and adoption of cloud and AI applications, it is imperative that both play a part in improvising for each other. The cloud has exposure to data sets that have never come out of traditional data centers. Similarly, AI needs loads of computing, storage, and distributed architectures (at times) to flourish and escalate further development.
The private cloud piece of the hybrid puzzle also has two ways in which AI technology is impacting it. To utilize AI applications effectively, the architectures, scale, and skills need to change immensely within the datacenters. Currently, GPU-based architectures are being touted to assist for the development of AI-based applications. Similarly, AI itself can be utilized to optimize and improve performance within the private cloud piece. It could be the rationalization and optimization of the datacenter to become energy efficient or to look at the best-fit architecture or something as critical as securing the private cloud by means of bolstering the cyber-security capabilities in real time using AI platforms.
Lastly, as AI-based services, applications, and systems increase in our IT environments and daily lives, the need for new architectures such as cloud-based edge and convergence of skills would continue to escalate as well. As companies across verticals strive for customer attention via their digital initiatives, the balanced (and the right fit perhaps) AI-hybrid cloud combination will enable them to stand out in a crowded market, both in terms of customer insights as well as service delivery in the long run.