Artificial Intelligence has come a long way in creating mobile apps, digital assistants, recommendation algorithms, process automation and a number of IoT enabled devices to create a connected world. Cloud computing+AI solutions have proven to be a boon in a variety of sectors, including e-commerce, software development, fashion, medical health, media/digital marketing, social networking, among others. 

AI-powered DevOps, machine intelligence and AI technologies integrated with a cloud computing platform can make development sprints more efficient and deployment more intelligent. Complex processes that need to be accomplished together by many departments can be streamlined by Cloud Analytics as well.

Let’s understand the basics of the two concepts and then explore how their combined power can help your business.

What is Cloud Computing?

The official definition of cloud computing is “the practice of using a network of remote servers hosted on the internet to store, manage, and process data, rather than a local server or a personal computer.” It’s as simple as using a digital cloud instead of physical storage devices. 

Artificial Intelligence is about building smart machines capable of performing tasks that don’t require human intervention.

How are the two important to each other?

Self-managing cloud

IT departments have several routines and repetitive tasks that can be streamlined using Cloud power and AI. As companies start to migrate to the cloud, they can rely on AI ML algorithms to automate their regular workflows using the analytical capabilities of a self-managing system so that time can be saved and invested in other core activities. Some companies that provide AI-based cloud solutions have managed to use cognitive computing for smart personalisation and self-learning algorithms that can create smooth process workflows. The combined power of AI and Cloud Computing is better than disparate or scattered work processes and storage systems that can easily be facilitated and merged in the cloud. 

Data processing and management

An organisation produces, sources or creates big data daily which needs an anchor in ML-based algorithms that can sift through the data dump to categorize the essential and weed out the unnecessary. GPU, CPU, cloud providers can provide powerful virtual support for chunks of data processing. AI embedded cloud storage systems can harness the power of big data to streamline project goals and deliveries, whether they be IT or software-related. Large data repositories can be catalogued and managed by data collection, input, processing and output all within the cloud once AI-powered features are integrated, resulting in the unified stream and batch data processing. Cluster management and auto-healing tooling embedded on a cloud platform can automate the data processing further.

 A new shift from cloud to ‘cloudlets’ can even allow some data to be even processed locally (on device) if 5G takes off.

Analytics-based insights

Cloud-based analytics can identify patterns and suggest actionable insights, by offering analytical tools and techniques to help companies extract information from vast data sets that would be impossible to handle using traditional methods. Cloud analytics is the use of remote computing resources to analyse real-time data. Businesses can take critical decisions and devise marketing strategies with the help of cloud analysis tools and increase their ROI. Business intelligence based on cloud analysis can get a huge boost due to smart data gathering, integration and analysis, all at lightning-fast speeds. A company’s entire infrastructure can be founded upon cloud analysis. Cloud deployment + AI/ML can drive the scalability of a business while maintaining the security of sensitive data. These days cloud providers are guaranteeing robust and fault-free availability 24/7 so that data analysis doesn’t get disrupted at any stage. Companies should look for natural language processing, custom visualization, secure data governance, embedded analytics in the public, private or hybrid cloud computing. Al/ML generated learning enables dynamic tooling which can, in turn, save hours on coding, debugging, testing and deployment. 

Cost-effectiveness

Companies have often found that cloud services can get expensive especially when they’re scaling up their business. AI and ML tools can prove to be the ideal cloud optimization solution as well as for real-time application resource management. Cloud-native start-ups who may not want to invest in physical IT infrastructure, but are hesitant to pay for expensive cloud computing, will be benefited in the future when more cloud companies start to offer affordable fees for their subscription models. It’s been predicted that 80% of all IT budgets will be invested in cloud solutions, making it imperative for cloud companies to embed AI/ML into their core constitution.

Ultimately, it boils down to the basics. Economically viable systems can be created by AI and the cloud, resulting in higher profit margins and increased customer satisfaction. Traditional data storage options are a no longer feasible option for organizations.