The twin forces of artificial intelligence and cloud computing are driving businesses forward on multiple fronts beyond IT. While the cloud market is poised to double in size to a staggering $947 billion by 2026, the AI sector is on track to expand more than fivefold, reaching $309 billion.

Instead of viewing these two technological powerhouses as rivals, however, business leaders should focus on understanding how the burgeoning artificial intelligence sector can forge a partnership with cloud technology to ignite unprecedented innovation.

The synergistic connection between cloud computing and artificial intelligence is rooted in automation. The deployment of AI optimises routine tasks, thereby boosting productivity and enabling IT professionals to concentrate on more groundbreaking initiatives.

It goes without saying that these two technologies are influencing each other in countless ways. Cloud investments are accelerating the uptake and expenditure on AI, leading to widespread applications of artificial intelligence – in fact, a Deloitte study revealed that 70% of businesses acquire their AI functionalities through cloud-based software, while 65% develop AI applications using cloud services.

The Unification of Artificial Intelligence and Cloud Computing

In order to automate tasks, artificial intelligence and cloud computing work together. It includes duties like data analysis, data management, and security. They also help with making decisions faster. AI’s capacity to employ machine learning and extract unbiased interpretations from data-driven knowledge propels efficiency in these operations and can yield substantial cost reductions across the enterprise. 

The application of AI software powered by machine learning algorithms within cloud ecosystems generates intuitive and interconnected experiences for customers and end-users. Siri and Alexa are two examples of this seamless integration that supports a wide array of activities, from conducting a search to playing a song to making a purchase. 

In ML algorithms, substantial datasets are utilised to train the model. This data can be unstructured, structured, or raw and necessitates formidable GPUs and CPUs for processing. Only an optimal combination of private, public, or hybrid cloud environments (based on security and compliance mandates) can deliver such immense computational capacity today. Moreover, cloud computing using artificial intelligence also facilitates services crucial to ML, such as batch processing, serverless execution, and container management.

Harnessing AI to Supercharge Cloud Performance

AI is the proverbial icing on the cloud cake — and also the filling, glaze, berries, and confetti rolled into one! That is why cloud computing with artificial intelligence is an unbeatable duo.

– Cost-effective

Historically, ML-based frameworks operated on costly hardware equipped with multiple GPUs within enterprise data centres. Thanks to advancements in virtualisation across both public and private cloud platforms, the expenses associated with developing, testing, and deploying these models have dramatically decreased. This has democratised access to AI for numerous small and medium-sized enterprises.

– Productivity

AI-driven algorithms historically demanded substantial administrative overhead for constructing, testing, and productionising environments, software upkeep, and allocating hardware resources for computational tasks and data storage. A centrally managed public cloud or hybrid cloud eliminates these burdens, freeing IT personnel to concentrate on non-routine responsibilities.

– Automation

Well, AI cloud computing is also being integrated directly into the infrastructure to automate standard procedures and optimise workloads. Within a hybrid cloud setup, AI tools can be deployed to observe, manage, and self-repair individual public and private cloud elements.

– Analytics

Data located within most cloud-based applications demands analysis for deeper insights. AI-powered models simplify the extraction of valuable information from this data in real-time, enabling the creation of integrated analytics and dashboards tailored to each application of artificial intelligence.

– Data Management

AI supercharges cloud-based operations across customer service, marketing, enterprise resource planning, and supply chain management by processing and generating data in real-time. For instance, AI tools embedded within Dataflow, Google Cloud’s streaming analytics platform, can power diverse functionalities such as algorithmic bidding in media advertising, fraud detection in financial services, threat prevention in IT security, and personalised product recommendations in retail.

– Enhanced SaaS Tools

The most apparent and widespread utilisation of algorithms within AI cloud computing is their integration into mainstream cloud-based applications to enrich functionality and user value. For example, Salesforce integrated “Einstein”, an AI-powered algorithm, into its core CRM platform to empower customers to decipher massive datasets, identify patterns within these datasets and extract actionable insights to optimise their sales strategies. This is merely one illustration among countless AI-enhanced cloud-based tools.

The Next Frontier: AI-Powered Cloud Computing

As cloud computing matures across the IT landscape, revenue expansion inevitably decelerates. Consequently, investors anticipate the surge of AI to revitalise cloud computing as leading tech firms increasingly strive to leverage artificial intelligence within the cloud. 

Amazon’s latest Bedrock cloud service exemplifies a significant stride in cloud-based generative AI. This platform empowers developers to integrate AI-generated text into their software applications seamlessly.

As organisations of all sizes heavily invest in AI, their respective IT teams must rapidly acquire the expertise and capabilities necessary to implement and scale AI cloud computing solutions effectively. Embracing AI technology sooner rather than later is paramount to ensuring the IT department possesses adequate time to integrate the technology before competitors gain a substantial advantage properly.

Overcoming Hurdles in AI Cloud Deployment

Network connectivity and data privacy are the key challenges faced in uniting AI and the cloud. 

– Connectivity

Any data processing system or algorithm operating in the cloud is entirely reliant on one critical factor: a stable internet connection. Erratic network performance can significantly impede ML processes and undermine their effectiveness, particularly in scenarios demanding real-time transactions and analytics.

– Data Privacy

The flexible, consumption-based model of SaaS technology empowers countless organisations worldwide to extract insights from data, optimise routine operations, develop innovative products, and even venture into new markets. Nevertheless, these businesses frequently process customer, supplier, and market data within cloud applications with little regard for the inherent security risks of the public cloud. 

Artificial intelligence expands these risks intensively within a public cloud environment when it does the job of processing the data into SaaS application. Sensitive corporate information could be exposed to a security breach or unauthorised access when the procedures and boundaries for AI cloud computing algorithms are not clearly defined. 

Winding Up

The strong partnership between AI and cloud computing is transforming industries and pushing technology forward. As artificial intelligence engineers keep innovating, the cloud provides the perfect platform to unlock their potential. This powerful combination is opening up new opportunities for businesses and individuals, from automating tasks to inspiring creativity. Both these transformative technologies are indeed powerful enough and play a significant role in AI. 

For more information on artificial intelligence, AWS, cloud computing and solutions, please visit the CloudZenia website.