Leverage Cloud Computing and AI Integration for Your Career: Best Courses to Upskill With

Cloud computing is a "plug-into" infrastructure that requires an internet connection to leverage on-demand computation, storage, processing and other similar services like renting physical and virtual servers, networking capabilities, development tools, etc., that is hosted remotely. The cloud service provider makes the resources available against a monthly subscription bill or fee. Cloud works on the ‘pay-as-you-go’ model, making it more flexible to use. It is well-known for its low cost, dynamic scalability, easy backup and restoration, improved collaboration, and lower maintenance. 

The 3 most popular models of cloud computing services are Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS). Countless companies are leveraging these services provided by top cloud providers like Amazon AWS, Azure, Google Cloud Platform, etc. 

According to Gartner, in 2022, IT enterprises will spend more than $1.3 Trillion in the shift to cloud services (which is more than half of the key IT market segment). To know more about cloud computing concepts, learn the Cloud Computing Basics by Coursera.

Artificial Intelligence (AI) is a machine-defined set of intelligence that simulates or mimics human behavior to automate certain operations. With the help of AI, Machine Learning (ML), and data-driven approaches, companies can help train machines to work intelligently. These algorithms take human interactions and other data and understand human behavior and traits. 

According to Gartner's report, more than 37% of enterprises have already enforced AI in their regular workflow. This adoption of AI has grown to over 270% in the past 4 years. Machine learning and deep learning implementations are reaching new horizons in almost every industry like healthcare, medical, cybersecurity, language processing, voice recognition, image processing, cloud, manufacturing, automation, and more. Explore Futurelearn's course on AI Design and Engineering with Microsoft Azure.

Cloud has become a boon for the branches of AI-ML and deep learning. For training ML and AI models with large datasets, it is essential to have massive storage that can scale up or down as per requirement. Cloud saves the day by potentially storing big data without any hassle. 

Let us now explore some of the benefits cloud technology renders to AI and ML:

Scalability and Elasticity

Almost all innovative AI training requires bulk datasets through which AI engineers and data scientists can train AI models. Storing such massive data amounts and dynamically scaling the storage often becomes a tough call in the case of on-premise servers. This is why companies prefer the cloud which can provide a better supply of data streams on AI projects.

Better Disposability of IT Resources 

Often AI projects employ enormous IT resources throughout their development life-cycle. Buying these resources, setting them up, and managing them in-house is a costly affair. Additionally, on disposal, the value of these hardware systems depreciates. This could unnecessarily push up the budget allocated for the AI project. But with the advent of the cloud, the Research and Development (R&D) cost decreases, computational quality improves, and prototyping of AI projects becomes faster.

High-End Data Processing

All the modern Cloud Service Providers (CSPs), render Graphics Processing Units (GPUs), and Tensor Processing Units (TPUs) in conjunction with CPUs for fast performance. While CPUs manage the regular functionalities of the cloud, GPUs help in rendering high graphics and data analysis operations. 

Cloud TPUs cater to high-end processing for training large neural networks and machine or deep learning models. Businesses do not have to buy separate GPU and TPU hardware for training neural nets and AI models. The ‘pay-as-you-go’ model provides the flexibility to pay for only the processing resources the project or model utilizes.

Companies usually fuel innovation in technology by uniting 2 or more technologies for more efficiency or by replacing legacy approaches with newer ones for accuracy. Many cloud providers and cloud technology leveraging firms started blending AI and ML with cloud systems to deliver an efficient output and reduce the overall business cost. 

Let us now explore some of the benefits of AI and ML within cloud technology.

Provide Deeper Insight

Almost all Cloud Service Providers (CSPs) leverage AI and data analysis to understand the customer base. It also helps them predict the requirement and accordingly allocate servers that best suit the need. 

The ML algorithms within these cloud engines and cloud systems residing in the data centers can take in historical data and understand the requirements of all dynamic customers. Some ML algorithms also perform a routine check to detect and troubleshoot or provide information about any issues seen in these large cloud infrastructures.

Enhance Cloud Security

Given the pace of cloud adoption, corporate leaders are also focusing on cloud security. To secure the cloud infrastructure and render the best service with 100% uptime, cloud companies and cloud leveraging firms started implementing intelligent ML solutions and customized security algorithms to detect different anomalies. 

These ML solutions utilize highly trained algorithms to detect malicious behavior within these large data centers and cloud-associated systems. AI and ML-based algorithms can also predict threats (insider or outsider both) and attack events within the cloud system through pattern recognition and malicious signatures. These systems can promptly respond to such security threats and prevent the entire system from shutting down.

Improve Cloud Allocation, Services, and Data Management

AI and ML can work together to measure and manage which server or computing system within the cloud can better fit customer requirements. Various AI and ML models help the cloud providers and cloud computing resource management firms to allocate and handle cloud resources to different customers. Cloud providers can also leverage AI to provide chatbots for real-time customer care and assistance. In the future, AI will be able to efficiently counsel corporate Chief Technological Officers (CTOs) in deciding which cloud components will best suit the business's needs.