Artificial Intelligence in Cloud Computing
The era of artificial intelligence is already upon us, and it’s found its home in the cloud.
Though artificial intelligence started much earlier than cloud computing, cloud computing and its technologies have improved AI very much.The term Artificial Intelligence (AI) was used for the first time by John McCarthy during a workshop in 1956 at Dartmouth College. The first AI application programs for playing checker and chess were developed in 1951.Over the years, there have been some investments in AI by vendors, universities, institutions. Sometimes, hopes were high and sometimes hopes were low. It was referred to as the artificial intelligence winter when there was not enough interest in AI from industries.
Google Drive is a very common example of cloud computing service that works with storage apps like Google Docs, slides, and sheets. Now the question arises, how can AI, which is the future of technology, add value to the cloud computing services? According to subject matter experts, AI might just be the technology to revolutionize cloud computing solutions.
Understanding AI and Cloud Computing
Let’s look at how cloud computing has been taking part to advance and enrich AI ingredients:
Cloud delivery models
1) IaaS (Infrastructure as a Service) helped AI practitioners to have an infrastructure environment — CPU, memory, disk, network, O/S easily so that a practitioner doesn’t lose time without waiting for an infrastructure team to prepare it. Moreover, cloud providers started to provide GPU resources later.
2) PaaS (Platform as a Service) helped AI practitioners to use AI and data science services including jupyter notebooks, data catalog services to develop new generation applications easily.
3) SaaS (Software as a Service) helped users to consume AI services within an application i.e. CRM, payment applications to create efficient results.
Cloud Technologies
• Containers: As the containers begin to separate applications in the computer space, the containers provide the same interaction with the environment for all data scientists. In addition, teams of data scientists may apply their containers to different cloud providers and even their favourite GPU capabilities.
• Kubernetes: Kubernetes is an open-source application for automated application, rating, and management of boxed applications. Since data scientists wanted to use data science platforms in a risky way, Kubernetes helped them. Kubernetes provides efficient use of natural computer resources including a GPU. Kubernetes also offers data science applications, platform applications that work to different cloud providers without having to worry about computer space.
- Data sets utilization: Data is the most important ingredient in AI. You need to have rich data sets to use your algorithms, models. Whether or not data sets are shared in the cloud, once you have found them you can store them in public or private cloud sites for easy access.
Why AI cloud
The most compelling benefits of AI cloud are the challenges it faces. It makes AI democratic, making it easily accessible. By reducing acquisition costs and facilitating collaborative creativity and innovation, it drives a powerful AI transformation in businesses.
The cloud really became a duplicate of AI, making AI-driven information accessible to everyone. Besides, although cloud computing technology is now much more advanced than using AI itself, we can safely assume that AI will make cloud computing more efficient. Businesses that invest in AI will receive double the cloud compensation; this makes the AI cloud very attractive.
The relation between AI and Cloud Computing today
According to Deloitte Global’s forecasts, some important numbers are:
- In 2020, 87% of companies were forecasted to use artificial intelligence;
- In companies that were forecasted to integrate artificial intelligence services in the Cloud:
70% will import resources through software
65% will start creating applications
Even in 2022, many people believe that artificial intelligence is still a thing of the future. However, the relationship between AI and the Cloud can transform business in productivity and efficiency.
The benefits of AI in cloud computing
Lower costs
A big advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be prohibitive with AI projects, but in the cloud enterprises can instantly access these tools for a monthly fee, making research- and development-related costs more manageable.
Intelligent automation
AI can automate complex and repetitive tasks to boost productivity, as well as perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. For example, IBM Cloud Pak for Automation provides prebuilt workflows for AI-powered automation.
Increased security
As enterprises deploy more applications in the cloud, intelligent data security is crucial to keep data safe. IT teams can use AI-powered network security tools to track and evaluate network traffic. AI-powered systems can raise a flag as soon as they find an anomaly. This proactive approach helps prevent any damage to critical data. For example, Amazon GuardDuty is an intelligent threat detection tool that uses AI and machine learning to find potential risks.
The downsides of AI in cloud computing
While combining AI with cloud computing provides numerous benefits, there are drawbacks as well. For example, while it can lower costs, AI is complex and may require a well-trained staff, which can cost more money upfront. The following are the other downsides to using AI in cloud computing environments:
Connectivity concerns
Cloud-based machine learning systems need consistent internet connectivity. Poor internet access can hinder the advantages of cloud-based machine learning algorithms.
While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.
Data privacy
AI applications require a large amount of data, which can include consumer and vendor information. For example, Amazon provides recommendations based on purchase history. While some data can be anonymous and can’t be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.
What We’re Expecting in Future?
Security will be front and centre:
With increased ransomware cyberattacks on IT infrastructure, companies will realise the need to establish zero tolerance for trust in their security strategy. They will invest in solutions that secure their crown jewels and protect their data through a single point-of-control, providing a holistic view of threats across environments.
Weaving data fabric into hybrid multi-cloud: Data fabric is emerging as the most innovative new architecture to apply AI in one place without companies ever having to move their data. A data fabric architecture is neutral to data processes, environments, usage, and geography and integrates core data management capabilities. It can help businesses investing in AI, machine learning, IoT, and edge computing get more value from their data.
Automation to augment workforce: Companies that will survive future disruptions will not necessarily be the fittest, fastest, or strongest but the most adaptable. Intelligent automation helps optimise processes, personalise customer experience (CX), and enhance decision-making to allow employees to engage in higher-value tasks. This includes leveraging AI for IT automation to help companies with real-time detection and automated response to IT incidents for faster resolution.
