According to the US National Institute of Standards and Technology (NIST), cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (such as servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.
Over the past decade, cloud computing has continuously pushed the boundaries of technology to provide ever-advancing solutions for networking challenges.
Thanks to the cloud, almost all organisations are today able to access high-performance computing power at affordable cost. This has spurred the hosting of key networking resources in the cloud, which has allowed organisations to create complex networks using only an internet connection.
Through applied data intelligence, these cloud-driven networks are now architected to power and visualise increasing amounts of meaningful data, which has allowed organisations to gain valuable business-related insights. They have helped to elevate the user experience and improve business models.
Cloud computing makes machine learning more accessible, flexible and cost-effective.
According to acclaimed technology author Gilad David Maayan, cloud computing is not only a field, it is also a development mindset that is able to create disruptions in its wake.
One of most disruptive technologies in the cloud-driven networking arena is artificial intelligence (AI), which is now helping to streamline network operations, while significantly enhancing the corporate mission.
For example, AI can be used to automatically provision and configure a wide range of cloud resources, monitor and optimise cloud performances and scale cloud services up or down to meet changing demand.
In addition, through intelligent automation, AI enables IT systems to deliver maximum uptime through the automation of complex and repetitive tasks, while providing methods such as self-healing to address challenges and problems that may occur.
AI can also speed troubleshooting through AI-powered data analytics, which can help to identify imperceptible patterns and trends. Importantly, cloud-based AI systems can learn and evolve over time, becoming more effective at processing data without the need for any human involvement.
In similar vein, AI is also central to the security and safety of cloud-driven networks. It plays an important role in this regard by identifying and blocking threats that might otherwise go undetected. AI also offers a robust, efficient and effective solution for anomaly detection in cyber security applications.
Turning to the business arena, AI assists sales, marketing and supply chain management teams to quickly and effectively compare historical and recent data and so gain beneficial intelligence. AI’s ability to recognise patterns and trends in data sets is also useful in this space.
When AI is aligned with technologies such as the internet of things, it is key to so-called “intelligent manufacturing” in smart factories which are more efficient, strategic and insight-driven.
In summary, the major benefits of AI in cloud-driven networks are increased flexibility and reliability with boosted performance and efficiency, thereby assisting organisations to achieve faster times-to-market and rationalise operations.
AI is also being used to power other cloud services, such as machine learning (ML) and incorporate it into cloud-driven networking strategies.
ML is a subset of AI that emulates human learning, allowing machines to improve their predictive capabilities until they can perform tasks autonomously, without specific programming. ML-driven software applications can predict new outcomes based on historical training data.
A cloud ML platform provides the computing power, storage and services required to train machine learning models. Cloud computing therefore makes ML more accessible, flexible and cost-effective.
What’s more, ML-derived data insights help corporate IT specialists easily identify and monitor the network operational state at a glance and thus contribute to the streamlining of business operations. As a result, ML is regarded as one of the most sought-after technologies today.
Unlike other cloud-based services, ML platforms are available through diverse delivery models, such as cognitive computing, automated ML, ML model management, ML model serving and GPU-based computing.
For instance, the cloud's pay-per-use model is capable of being used as a platform for companies that wish to leverage ML capabilities for business. It provides the flexibility to work with the above ML functions without the need for advanced data science skills.
In this way, enterprises can first test and deploy smaller projects in the cloud and then up-scale as need or demand increases. The pay-per-use model further makes it easy to access more advanced capabilities without the need to bring in sophisticated hardware.
AI and ML are two of the trending enterprise technologies today. They have sparked widespread interest based on the promise of improved efficiencies and lower costs.
Some of the fastest-growing applications for AI and ML are in the fields of e-commerce and advertising, where the analysis of growing volumes of business data that already resides in the cloud obviate the cost and complexity associated with moving it in-house.
Looking ahead, the range of AI and ML services offered in the cloud will significantly widen and grow. The push from large public cloud companies to provide AI and ML tools and models will continue until they become vital and indispensable elements of future cloud-driven networks.