How is Artificial Intelligence Used in Cloud Computing?

The cloud enables the instant set-up of applications with reduced costs. Enterprises can leverage easy scalability, improved efficiency, and increased security. The cloud also provides an easy way to store the ever-increasing data volumes and access them from anywhere. 

Over the years, the cloud has become a powerful tool that enables enterprises to realize the potential of AI. A key application area that has enhanced the value of the cloud is driving digital transformation in enterprises. Industry 4.0 has made digital transformation inevitable for enterprises. The cloud is the key driver of digitization, helping enterprise become part of an interconnected digital ecosystem. For instance, applications involving speech and image recognition, automated vehicles, natural language processing, and medical diagnosis require processing vast volumes of data in real time. The cloud and the latest innovations, such as edge computing, enable such computational power.

The cloud-AI landscape is expanding by the day and will grow at a CAGR of 20.3% by 2027. Here are the top ways AI allows enterprises to use the cloud better.

1. Greater flexibility and agility

Cloud AI betters infrastructure management and allows businesses to be more agile and flexible.

Cloud computing delivers computing services, including storage, servers, database, networking, and analytics. Enterprises using the cloud instead of on-premises infrastructure can scale up or down resources as needed. They can roll out applications without waiting to set up the infrastructure stack upfront. The enterprise can spin up more servers in response to increased traffic. The cloud facilitates the instant action needed to secure early mover advantage.

Artificial Intelligence ensures process efficiencies that minimize errors and improve productivity. AI-powered algorithms build up on the inherent flexibility and scalability of the cloud. Using cloud computing and artificial intelligence in combination automates routine activities easily. AI tools monitor and manage private and public cloud computing services, enabling running everyday tasks independently. It also optimizes storage and structure and offers real-time insights to improve business decisions. The benefits include improved day-to-day experience for customers.

Cloud AI enhances efficiency and makes enterprise systems more agile and flexible. It also accelerates the productivity of the workforce. Enterprises may also leverage AI’s deep analytical capabilities to track consumer behavior and improve personalization. Recommendation engines get better. All these benefits occur while enhancing process integrity at the same time.

The Saas model makes hosting and accessing analytical software easier.

  • Cloud AI optimizes workflows and produces real-time insights that transform day-to-day operations and re-imagine end customer experience. AI-as-a-service, or the delivery of Artificial Intelligence applications through the cloud, sets the stage for development and competitive differentiation through innovation.
  • Cloud-based infrastructure as a Service (IaaS) models provide AI with CPU, memory, disk, network, O/S, and GPU. These ready-to-operate infrastructural elements help businesses hit the ground running with their AI technology deployments. Platform as a Service (PaaS) likewise supports AI with data science services such as jupyter notebooks and data catalog services, making it easier to develop new-generation applications. Software as a Service (SaaS) helps users consume AI services within CRM or other applications.
  • Cloud technologies such as containers and Kubernetes further ease AI deployments in enterprises. Containers isolate applications from computing environments, allowing data scientists to run applications independently on different cloud providers without worrying about the computing environment. Kubernetes, the open-source system, automates the deployment and management of containers. Kubernetes enables using GPU and other computing environment resources efficiently. 

2. Enabling intelligent automation

Artificial intelligence ensures smooth workloads and automates repetitive processes.

Cloud AI synchronizes AI tools and software with the power of cloud computing. A well-integrated cloud-AI model removes the barriers to intelligent automation.  Cloud AI:

  • Offers real-time insights based on historical trends, patterns, and other insights not decipherable through conventional methods. Enterprises may roll out cognitive automation on semi-structured and unstructured tasks to transform the end customer experience. A cloud module coupled with AI capabilities analyses demands and available resources without human interference to provide the most optimal recommendations and value-added services to customers. The cloud facilitates AI by ensuring infinite processing capabilities with easy scalability.
  • Manages and processes large data sets quickly. AI algorithms self-learn from transactions and get better with time. Such self-learning systems do not require human intervention or programming effort after the initial effort. The cloud provides such self-learning systems with infinite capacity and easy accessibility from anywhere. Users may control such cloud-based intelligent systems from any remote corner of the globe. Examples include home assistants such as Amazon Alexa and Siri. 
  • Offers developers the tools to create applications on any cloud platform. 
  • Eases integration challenges. Integrating disparate technologies is always a challenge. When enterprises move these applications and technologies to the cloud, integration challenges ease. The data scientist can add an AI layer to the cloud above the tech stack. 

Enterprise users may use these features to accelerate innovation. Often, a difficult tech stack becomes a barrier to innovation. When the project team trying to roll out an uncertain product has to worry about the tech stack, their difficulties compound and often reach the breaking point.

Hosting AI applications on the cloud may pose challenges in integrating legacy on-premises applications. Often, data privacy concerns, lack of expertise in building custom models, and high upfront costs associated with building complex Machine Learning models using custom hardware resources like GPUs make the public cloud computing services unsuitable for running AI-powered applications. Hybrid cloud models remove such stumbling blocks.

A hybrid cloud, which combines public and private clouds, offers businesses the flexibility to use different resources depending on their requirements. Enterprises may run applications and workloads on-premises or in public cloud computing services without worrying about data storage location. They may use public cloud workloads and infrastructure as a service (IaaS) for non-critical applications and move legacy applications and workloads from on-premise servers to any cloud platform seamlessly. 

3. Improved performance

Artificial Intelligence makes machines capable of mimicking human intelligence. But AI technology is only as good as the data ingested by the algorithms. Unless the enterprise can train and later feed the algorithms with current and relevant data, the insights will be flawed. 

Training algorithms using a large data set enables the software to perform tasks involving human intelligence. Consider facial recognition software. The trainers feed the software algorithm with thousands or even millions of images. The software compares a new face with these training images and unearths a match. In the early days, human supervision may validate the algorithm’s selection. When the algorithm adds this new image to its database, it becomes more mature, and the next match becomes even more accurate. The process repeats with every transaction until the algorithm makes perfect matches.

Deep learning, a subset of AI, allows computers to learn from large data sets and perform complex tasks such as image recognition or speech analysis. Predictions get better and more accurate as the cloud feeds more data into Artificial Intelligence systems. 

The cloud offers resources and infrastructure that ensure better performance of AI algorithms.

  • Data storage capabilities. Training algorithms and running them requires vast storage and processing capabilities. Provisioning these resources in-house becomes prohibitively expensive and non-viable for most companies. Cloud computing offers these resources. Hosting advanced computation techniques and powerful GPUs in the cloud makes the products more efficient and effective. 
  • Self-healing capabilities. AI may self-heal. Private and public cloud use cases can rely on AI to monitor and manage their instances and self-heal when a problem occurs.
  • MLOps. DevOps combines software development and IT operations. Machine Learning Operations (MLOps), an extension of DevOps, offer extensive data science and machine learning platforms and API services. Most cloud providers, including AWS, Azure, GCP, IBM, and Oracle, provide such cloud environments. Many top analytic firms such as IBM, RapidMiner, and SAS also provide their data science and machine learning platforms on different cloud providers.
  • Edge capabilities. Conventional cloud is fast becoming inadequate for the data-intensive applications of Industry 4.0. Edge computing extends the cloud capabilities to on-premise with low latency and offline capabilities, enabling more use cases such as video analytics. The edge infuses enterprises with more computing capabilities than what the conventional cloud offers. 
  • Virtualization, which does away with compatibility issues. Cloud-based virtualization makes sure everything works together seamlessly 

In the future, quantum computing will advance AI even more from current levels.

4. Better data management

Managing data is a huge challenge for today’s enterprise, considering the ever-increasing volumes. From 2020 to 2022, the data generated by an average enterprise will increase from one petabyte to 2.02 petabytes. Enterprises already rely on the cloud to store such huge data volumes. The cloud improves accessibility to data. Integrated cloud depositories enable anytime, anywhere access to data.

In most enterprises, the IT stack grows organically over time. Disparate systems may need more integration. Often, shadow IT may have emerged as some departments try to bypass IT to get things done faster. During mergers and acquisitions, the integration team may leave something incomplete. Such organic growth, however, cause unstructured, siloed, or incomplete data. Such information blocks inhibit analytical tools from accessing the correct data.

Artificial Intelligence streamlines data management to offer better process efficiency and deeper actionable insights. Integrating AI technology with cloud computing services boosts the accuracy of database queries and performance. Cloud-based data lakes serve as an integrated repository of data. Enterprises may consolidate their data in such integrated databases. AI-powered tools and algorithms may access the needed data from such depositories from anywhere, at any time, to get access to live, relevant data. Side-by-side, there is lesser consumption of system resources.

Cloud AI:

  • Offers tools and applications for easy data management. These solutions identify, update, catalog, and classify data in real-time.
  • Offer dynamic data-related functionality to end users. Many SaaS providers now add AI layers to their products and offer exceptional functionality to end-users and customers. For instance, AI tools leverage customer data from CRM suites to deliver personalized insights.
  • Enables generative modeling, or the ability to create new data based on existing datasets. The algorithms use rules to generate realistic examples similar to the original dataset. Such applications find use to generate images, for natural language processing, and in video synthesis.
  • Powers generative adversarial networks or combining multiple neural networks to generate new, realistic data. One network generates new images or videos. The other network assesses the quality of these new images based on predefined rules. During training, these networks compete against each other and get better.

5. Cost-effectiveness

Artificial Intelligence offers great potential but is also cost-intensive. AI deployments need huge processing capacities, including databases and servers. Setting up such resources in-house is prohibitively expensive for most companies.

Cloud computing allows:

  • Easy provisioning of resources at scale. Users pay only as much as they use. Enterprises rolling out AI-powered applications may subscribe to cloud stacks and shed the same when the need is over. They do not need to absorb the costs or go through the hassles of setting up servers, databases, and the connected infrastructure and then spend a fortune maintaining the same. The cost savings find better use for the strategic development of AI tools and accelerators that generate greater revenue.
  • Subscription-based models. The cloud also transforms CAPEX into OPEX. Many enterprises cannot afford to make capital investments to increase capacity. But they can easily subscribe to cloud-based services. The payment for cloud-based services is easily attributable to operational expenses. Such a subscription-based model also makes R&D more affordable.
  • Risk-free deployments. The cloud also enables risk-free deployment of AI models. Artificial Intelligence deployment is always risky. Very few models go beyond the proof-of-concept stage. When a model does not work out, the investment to date goes down the drain. The cloud enables strategic planners, and business managers can deploy AI technology as part of their product strategy easily, with limited downside. There is no risk since costs do not skyrocket if demand increases unexpectedly. Even if the project fails, the downside is only the cost of the cloud subscription.
  • Improved task automation. Integrating Artificial Intelligence and cloud computing improves task automation. Machine Learning algorithms create self-learning models that get things done with the involvement of minimal people. Getting things done with a lesser workforce reduce costs and improves accuracy. Process efficiency improves.

6. Improved security

Data security is a big challenge in the cloud. Storing sensitive information such as personal information, trade secrets, or financial details on the cloud is risky since the enterprise loses direct control over the data. Such a move may also violate several countries’ data protection laws or regulations, which forbid transferring such sensitive data outside specific geographical boundaries.

The integration of Artificial intelligence with the cloud enhances network security. AI-powered network monitoring tools:

  • Indulges in error spotting and identifies threats in the network in a much better way than conventional security tools. AI-powered solutions identify malicious activities and potential risks and enable prompt countermeasures. These tools detect and block unusual events or interference in real time.
  • Automates fraud detection. AI-powered monitoring systems identify out-of-place patterns.
  • Enforces superior access management to prevent unauthorized access and limit the fallout of human errors.
  • These AI-powered applications are resource intensive and require always-on capabilities. The cloud, with infinite storage capabilities and easy scalability, is tailor-made to deliver such solutions.

The success of Cloud AI security deployments depends on charting out a privacy policy that outlines how the enterprise will handle data, who will use the data and how, and who has access to it.

Cloud AI offers businesses more functionality and the ability to provide better value to clients. AI adds to the already robust capabilities of the cloud and offers unmatched value to end-users. But a one-size-fits-all approach does not work for cloud AI. Enterprises must move their tech stack to the cloud to best suit their operational and strategic convenience. Also, synchronizing AI and the cloud requires significant expertise, resources, and cost. Once the enterprise gets integration right, they get potent machine-learning capabilities that deliver competitive advantages.


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