December 13, 2022
Relation between cloud computing and artificial intelligence
Cloud computing is a fairly new service that organizations and businesses are adopting at a rapid pace. It’s become popular [1] for several reasons, including speed, efficiency, and cost savings. When used correctly, it becomes a great tool for storing, accessing, and using information. And when integrated with artificial intelligence, they form a deadlier combination that allows users to manage their data, point out patterns and insights from the data, optimize workflows, and much more.
For businesses, the relationship between cloud computing and artificial intelligence means an increased capacity to compete with key market players in their niches. But how exactly do the two technologies layer up to bring these immense benefits?
Read on as we take a closer look at what you need to know about the relationship between AI and cloud computing.
Key Takeaways
- Cloud computing provides scalable infrastructure, storage, networking, and managed services, while AI uses data and models to generate predictions, recommendations, or automated actions.
- Combining AI with cloud infrastructure gives organizations flexible access to computing resources without requiring large upfront investments in on-premise hardware.
- Cloud-based AI can support data analysis, forecasting, automation, personalization, and intelligent business reporting.
- The most suitable cloud platform depends on workload requirements, existing technology, data location, security, scalability, specialist skills, and total operating cost.
- Production AI systems require MLOps processes covering model versioning, testing, deployment, monitoring, retraining, governance, and rollback.
- Cloud adoption does not automatically reduce costs or improve reliability; organizations must actively manage resource usage, access controls, data quality, availability, and model performance.
Cloud computing vs. artificial intelligence
Artificial intelligence and cloud computing are often mentioned in the same breath. Although it’s hard to tell which one is greater than the other, it’s quite obvious that artificial intelligence has surpassed cloud computing by a great margin when it comes to popularity.
To understand the different roles that the two technologies play in the modern business landscape, we need to look at them independently.
AI and its applications
In simple terms, artificial intelligence [2] is a subset of data science that leverages machines’ and computers’ capabilities to mimic the human mind’s decision-making and problem-solving capabilities.
Its applications are fast extending towards many industries and have reached a level where it can learn how businesses and manufacturing plants work, provided its fed with sufficient and accurate data. When combined with other technologies, such as machine learning and IoT[3], it yields some incredible results and saves business owners a lot of money, effort, and time.
The rising popularity of AI can be seen in a wide range of commodities surrounding us. These include self-driving cars, virtual travel booking agents, smart assistants, and much more.
Cloud computing and its applications
Cloud computing is a technology that allows businesses to rent instead of buy their IT infrastructure and software. Unlike on-premise data centers that require considerable upfront investment [4], cloud computing offers user-oriented subscription services that allow users to pay for it as they use it. These services include but are not limited to databases, networking, business intelligence, and data analytics.
Cloud computing is gaining popularity in different sectors mainly due to its affordability, flexibility, and accessibility. Besides its short deployment times, it allows businesses to move their operations to a virtual office. This way, employees worldwide can access the system and work remotely.
Cloud computing VS. AI: Which one is better?
Cloud computing and AI serve different purposes. So, it’s not really a question of which one is better between the two, but rather how they complement each other. When used together, the two technologies can help business owners address various pain points in different aspects of their business.
The benefits of AI in cloud computing
If you’re wondering why there’s so much hype about the use of AI and cloud computing in unison, then perhaps you need to look at it from a practical point of view: Despite having a robust cloud strategy that could potentially increase your organization’s productivity, security, and efficiency, you’re more likely to fail at translating the insights you get from your data into useful information Reason being?
Without the improvement processes on the cloud that come from the use of artificial intelligence, it becomes extremely difficult to reflect on the benefits of cloud computing.
That being said, here are the top benefits that come from using AI and cloud computing together:
Seamless data access and data management
AI is all about leveraging data from different sources to make more intelligent business decisions. Cloud computing, on the other hand, unlike on-premise data storage, can hold significant quantities of data. This means that if AI is integrated into the cloud, organizations can better control and manage the data available at their disposal.
The hybrid cloud environment could learn from the collected data, make estimates, and solve potential issues in advance, all thanks to AI. AI and machine learning are also great at transferring data between the cloud and on-premises infrastructure, helping organizations to better manage and control their data. The result is better data insights, scalability, and the ability of organizations to better utilize resources available at their disposal.
Cost savings
Cost savings is perhaps one of the most compelling reasons why more organizations are using AI in the cloud. For businesses looking to incorporate artificial intelligence and machine learning into their operations, the economic benefit of using AI and cloud computing in unison goes beyond work automation.
Unlike on-premise data centers, cloud computing presents a cheaper option for implementing AI projects. That’s because it uses a pay-as-you-go model that eliminates upfront costs such as hardware administration and maintenance, which are quite costly when an organization uses on-site data centers.
Better decision making
AI learns from historical data to make predictions about future states. Given the huge number of data sets available on the cloud, combining AI with cloud computing exposes machine learning algorithms to data that could help develop a gradual understanding of the problems at hand.
Based on the data characteristics, AI can be quite helpful in making informed decisions and generating valuable recommendations for customers. Organizations can also use cloud business intelligence solutions to transform distributed data into accessible reports, visualizations, and insights that support faster decision-making. When properly validated and monitored, AI-assisted analysis can reduce processing time and help teams identify patterns that would be difficult to detect manually.
Increased reliability
Cloud computing presents a quicker and easier way for organizations to store their data. Unlike on-premise data storage, you can always access your system from another server if there are any issues or damage within your system.
Using AI in cloud computing also allows organizations to integrate all their software data and other internal tools into the cloud, making it easier for all team members to access them. The result is a more reliable and flexible work process.
Final thoughts
The future of business relies on the adoption of technologies such as artificial intelligence and cloud computing. While the two technologies have separate applications, integrating them has proven to be more advantageous than using them separately.
Some of the benefits that organizations stand to gain from using AI in the cloud include seamless data access, cost savings, better decision-making, and increased reliability. So, if you want to take your business to the next level, you shouldn’t shy away from adopting these technologies! Discover more about our AI consulting services.
References
[1] Accenture.com. Wat is Cloud Computing and Why is it Important?. URL: https://accntu.re/3W8TNSU. Accessed November 30, 2022
[2] Oracle.com. What is AI. URL: https://www.oracle.com/ke/artificial-intelligence/what-is-ai/. Accessed November 30, 2022
[3] Tectaret.com. What is IOT. URL: https://www.techtarget.com/iotagenda/definition/Internet-of-Things-IoT. Accessed November 30, 2022
[4] Arcserve.com. Data Centers. What are the Costs of Ownership. URL: https://www.arcserve.com/blog/data-centers-what-are-costs-ownership. Accessed November 30, 2022
FAQ
Should a company choose public, private, or hybrid cloud for AI workloads?
A public cloud is often suitable when an organization needs rapid access to managed AI services, scalable computing power, and flexible usage-based billing. A private environment may be preferred when workloads involve strict data residency, isolation, latency, or regulatory requirements. A hybrid approach can keep sensitive data or critical systems on-premises while using public cloud resources for model development, training, or selected inference workloads. The decision should follow a documented assessment of security, integration, performance, cost, and operational capabilities.
What should be compared when choosing a cloud platform for AI deployment?
Organizations should compare the available model services, GPU and accelerator options, data platforms, MLOps capabilities, regional availability, integration with existing systems, security controls, specialist skills, support, and pricing structure. They should also determine whether the platform supports the complete lifecycle—from experimentation and training to controlled deployment, monitoring, retraining, and rollback.
Is running AI in the cloud always cheaper than using on-premise infrastructure?
No. Cloud services can eliminate major upfront hardware purchases and allow organizations to scale resources according to demand, but poorly controlled workloads may generate substantial costs. Training jobs, continuously running inference endpoints, GPUs, duplicated data, network transfers, logging, and idle development environments can all increase spending. Cost estimates should therefore include the complete lifecycle of the system rather than only the initial deployment.
What security controls are required for cloud-based AI systems?
Organizations should apply identity and access management, least-privilege permissions, encryption, network segmentation, secure credential storage, audit logging, vulnerability management, and controls over training and inference data. Monitoring should cover both system performance and suspicious activity affecting models, data pipelines, endpoints, and user access. Regular security reviews are also needed as the architecture, models, and connected services change.
Why is MLOps necessary for AI cloud deployment?
MLOps introduces repeatable processes for preparing data, tracking experiments, validating models, managing versions, deploying releases, monitoring production performance, and retraining models. Unlike a traditional software application, an AI system may lose accuracy as input data and real-world conditions change. Automated CI, CD, and continuous training pipelines help organizations update models consistently rather than relying on manual deployment procedures.
What should be monitored after an AI model is deployed in the cloud?
Teams should monitor endpoint availability, latency, error rates, throughput, infrastructure utilization, and cost alongside AI-specific measures such as input-data drift, prediction quality, model performance, and differences between training and production data. Monitoring rules should define thresholds and alert responsible teams when degradation or unexpected behavior occurs.
How can a company reduce cloud vendor lock-in for AI workloads?
A company can separate its business logic, data layer, model interfaces, orchestration, and deployment infrastructure instead of tightly coupling the entire application to proprietary services. Portable containers, documented APIs, infrastructure as code, open data formats, and independent model evaluation processes can make migration easier. However, complete portability may increase complexity and prevent the organization from using some valuable managed services, so the acceptable level of dependency should be defined intentionally.
How can cloud reliability be improved for production AI systems?
Production systems should be designed around the required availability target, with appropriate redundancy, automated recovery, health checks, capacity planning, backups, and tested rollback procedures. Model services should also define what happens when an endpoint, data source, or external AI provider becomes unavailable. Reliability means not only keeping the infrastructure online but ensuring that the system continues performing its intended business function correctly and consistently.
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