What Are Cloud Computing and Edge AI?

What are cloud computing and edge AI? Cloud computing refers to instant access to various servers, networks, and applications over the internet. Edge artificial intelligence (AI) is the deployment of AI algorithms and models on local edge devices. These devices process data in real-time and perform data analysis without the need for cloud infrastructure. Discover the uses of cloud computing and edge AI, and learn how to develop your own applications utilizing cloud and edge services. 

What are cloud computing and edge AI used for?

Edge AI performs machine learning (ML) tasks by combining AI and edge computing. Edge computing refers to an architecture that allows data processing closer to the source it came from to help solve some of the challenges associated with working with large amounts of data, including bandwidth and latency issues.

Edge AI and cloud computing have various uses, including:

• Data security: Edge AI executes security tasks such as identifying unusual activity using computer vision and object detection. 

• Real-time data processing: Cloud computing and edge AI improve response times and develop rapid insights from data. 

• Efficiency improvement: Edge computing improves workflows by quickly processing mass amounts of data local to the site where the data was collected. 

Understanding cloud computing and edge AI

Cloud computing and edge AI systems have various components, including edge devices, servers, processors, and routers. 

Key features of cloud computing and edge AI

Some key features and hardware used within cloud computing and edge AI include: 

• Edge devices: These devices control the flow of data to optimize data transitions. Some examples of edge devices include drones, smart cameras, and various other Internet of Things (IoT) devices. 

• Servers: These systems process data local to its origin. For example, if the data is from a manufacturing company, the server can process it on the factory floor. 

• Processors: Processors consist of central processing units (CPUs), graphics processing units (GPUs), and storage for memory. CPUs establish how the edge computing system performs, and GPUs enhance the hardware within the system to enable performance computing. They work together to power the computing devices. 

• Routers: Edge routers connect networks to enable the use of computing devices, often connecting networks to the internet or a wide area network (WAN). 

How edge AI differs from traditional AI

The main distinction between edge AI and traditional AI used with the cloud is that edge AI integrates models into its framework and deploys data directly on edge devices instead of relying on the cloud. Utilizing local devices enables lower latency and decreases bandwidth use. Edge AI implements local data processing, while traditional cloud-based AI utilizes the cloud to process data, which can increase data security risks. While the cloud comes with various security risks, it can also enhance system performance. 

Core technologies in cloud computing and edge AI

The use of cloud computing and edge AI involves utilizing cloud providers and services while considering data processing power and latency concerns. 

Cloud providers and services

Some cloud providers and services available to you include Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and IBM Services for IBM Cloud. 

• AWS: This is the world’s most widely used cloud provider. It enables users to optimize workflows while being more Agile, resulting in faster innovation. AWS provides various services and technologies, such as AI, IoT access, and ML, to create cost-effective solutions and streamline system performance. 

• Microsoft Azure: A cloud computing and edge AI service utilizing AI-powered applications that maintain excellent security standards while managing systems. You can also build your own generative AI applications with Azure. 

• Google Cloud: A cloud platform you can use to build AI agents to improve customer experiences, rapidly analyze and process data, and write, debug, and run applications on local edge devices or through the cloud. 

• IBM Cloud: A cloud consulting service that allows you to manage and refine applications on various platforms. IBM Cloud helps to improve scalability and time to market and can reduce costs for organizations. 

Data processing and latency considerations

The number of devices connected to the internet and the volume of data produced by these devices affect the latency rates of data centers. With edge AI, organizations are able to streamline data processing and improve latency rates by moving the data center closer to where the data needs to be processed.

Who uses cloud computing and edge AI?

Various industries utilize cloud computing and edge AI, including: 

• Manufacturing: Manufacturers use cloud computing and edge AI to optimize operations, enhancing the efficiency of their processes. 

• Retail: Retailers utilize edge AI and cloud computing to enhance the customer experience through advanced applications like smart shopping carts and smart check-out stations. 

• Health care: The health care industry implements edge AI in its systems to optimize patient stabilization processes, reduce data privacy risks, and enhance the data processing abilities of emergency vehicles. 

Pros and cons of using cloud computing and edge AI

Some advantages of utilizing cloud computing and edge AI include: 

• Real-time data processing: Users can perform instantaneous data analysis with no need for systemic integration. 

• Scalability: Edge AI leverages cloud-based platforms to simplify the process of scaling systems. 

Some disadvantages of using cloud computing and edge AI include: 

• Data accumulation: Organizations must closely monitor what data is important and what data is irrelevant. If proper data management practices aren’t instituted, the system can become overloaded with unnecessary data. 

• Bandwidth issues: If an edge device has poor or inconsistent connectivity, users may not be able to access their data in a timely manner. 

Getting started with cloud computing and edge AI

Get started with cloud computing and edge AI by learning to build your own AI solutions in the cloud or on local edge devices. 

Tips for building AI applications in the cloud

You can set up cloud-based AI environments using various cloud services, including IBM, Google Cloud, and Google AI Edge. IBM provides a service called watsonx.ai that enables you to develop and implement AI-powered services. You can also learn how to perform predictive analytics with AI tools on IBM’s website. 

On the Google Cloud website, you can learn how to build and deploy generative AI applications. Google AI Edge is another service that enables you to deploy your applications across mobile devices and the web. 

Learn cloud computing and edge AI with Coursera

Cloud computing provides immediate access to servers, networks, and applications online, while edge AI deploys applications on local devices to process and analyze data in real time without relying on cloud infrastructure. 

Expand your understanding of cloud computing, including its architecture and security, with the AWS Cloud Technology Consultant Professional Certificate, or learn more about how to deploy applications with Cloud Native methodologies via IBM’s IBM Full Stack Software Developer Professional Certificate.