It seems that everywhere we look these days, artificial intelligence (AI) and machine learning are being touted as the next big thing. You can compile mountains of data, use an AI-based system to detect patterns and problems, and even begin to recommend solutions.
In the financial technology world, AI is being used to power chatbots for consumers who want to check their account balance or even pay their bills. Machine learning is being used to turn those strange codes on your credit card bill (***SBUXPDX1928***) into something readable by humans (Starbucks, Portland Oregon).
It’s being used in home automation, marketing, regulatory and compliance monitoring, and even the oil and gas industry.
AI and machine learning are also being leveraged to make cloud computing more efficient and effective, eliminating the need for dozens of computer professionals to work round the clock to keep entire server farms up and running.
Artificial intelligence is what makes it possible for cloud servers to learn and adapt as new inputs and information become available through users’ interactions, information, and transactions. By using this technology, computers can maneuver through a vast amount of data and quickly recognize important patterns.
For example, this is how cloud computing companies combat cyber attacks on their clients. It’s also being used by Since they are servicing so many customers across a wide range of industries they are able to gather a larger amount of data than if a company were to implement a similar system in-house. This information is utilized to then pinpoint patterns for a variety of different types of attacks. This is how a cloud security system can detect the difference between, say, a hacker trying to break into their system versus a remote employee who just typed in the wrong password.
It would be near impossible for a similar security system to be established in-house because they wouldn’t have access to the vast amounts of data available to the cloud databases.
AI can also help a cloud system reduce traffic congestion, manage storage issues, synchronize backup data, and even recognize when there are network issues that might require human intervention to avoid a system failure, such as recognizing when a server is getting too old or slow, signaling an imminent failure. AI systems are ideal for helping cloud systems self-manage, self-repair, and self-optimize.
According to an article on Wikibon.com:
Without AI’s ability to perform continuous log analysis, anomaly detection, predictive maintenance, root cause diagnostics, closed-loop issue remediation, and other critical functions, managing complex multi-clouds may become infeasible or cost-prohibitive for many organizations. — James Kobielus, Building AI Optimization Into Your Cloud Computing Infrastructure
This is one of the best selling points for using a cloud platform. Not only do cloud servers have financial benefits, operationally scale up and down as needed, and have the capability to back up data in multiple locations for added protection, but they are quickly learning how to identify attacks and breakdowns at such a large scale that they can nearly identify them before they even happen.
Cloud computing opens up more options for how to respond to the different issues IT departments often have to deal with. Rather than having a large staff manage everything in-house, artificial intelligence and machine learning can help you handle it all with a smaller staff and a smaller budget.
To learn more about how cloud computing can greatly improve your company’s IT requirements at a lower cost than doing it yourself, you can talk to one of our representatives about what Value Global can do for you.
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