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How Will Machine Learning Impact the Future of the Cloud?

Machine learning and cloud computing are often things people think of separately. But, technological advancements show machine learning has the potential to impact the cloud's future in ways people may not expect.

It Will Assist With Data Management

For many companies, moving data to the cloud is only part of the goal. They want to use that information effectively once it reaches the cloud, and machine learning could make that aspect easier. A startup company called AtScale already offers data management, but it has its eyes on the future and plans to depend on machine learning for enhanced offerings.

More specifically, the company wants to use machine learning to serve up data quickly, based on the queries provided. Ultimately, the tools could help companies tap into their data for more effective decision-making.

Also, Amazon recently launched a machine learning solution for data management when it introduced a service called Amazon Comprehend Medical. It's geared toward the health care industry and combines machine learning with text analysis to extract details from patients' records - such as diagnoses, treatments and medications - once those documents get uploaded to the cloud.

Those are just a couple of examples of how machine learning could help cloud customers get even more insights about their cloud-stored data through powerful tools that emphasize simplicity. Now that service providers can innovate with machine learning, they'll likely position the technology as a perk and competitive advantage for clients.

It Could Create Privacy Concerns

Almost all technological innovations have pros and cons to weigh, and the blending of machine learning with cloud computing is no different. Some analysts fear the combination of the two may erode people's privacy. Whether it does largely depends on how companies use the data. For example, some cars automatically track driver data, but what if they shared it with an insurance company and affected premiums?

It's not always clear to customers whether a party that stores data may share it with other entities. Although it's often possible to find that all-important detail in a terms-and-conditions document, many people don't read that information after initially seeing it or know how to refer to it later.

Additionally, even if customers are aware their data gets shared, will they have control over whether and when it happens? That unknown factor creates uneasiness.

Machine Learning May Increase the Cloud's Reliability

Before clients choose cloud providers, they often look at the documentation associated with the service and get details about uptime guarantees. After all, if people move their data to the cloud but then cannot access it due to outages, it removes the convenience factor.

It's likely cloud companies will increasingly depend on the cloud to give warnings about when things could go wrong before they happen. In other words, 2019 could be the year of the self-healing cloud. Perhaps, when conditions are amiss in the cloud environment and machine learning identifies them, providers could automatically take corrective actions before the worst outcome occurs.

Cloud-Based Machine Learning Could Exacerbate an Existing Skills Shortage

When companies decide whether using machine learning in the cloud is the right approach for them, they need to realize that, although providers are making it easier than in the past for people to take advantage of machine learning tools that exist in the cloud, doing so is not necessarily straightforward and may require professional guidance.

If there are more efforts to bring machine learning to the cloud during the year ahead, that trend could make the already-severe shortage of machine learning professionals more pronounced. To get an idea of how substantially the demand for those employees has risen, consider that from 2012 to 2017, it went up by 9.8 percent, according to data from KDNuggets.

The shortage has compelled some companies to accommodate for the gap by looking for machine learning talent from other countries.

Machine Learning Has the Capability to Make the Cloud More Secure

Cybersecurity is a growing concern, both for companies that have moved much of their operations to the cloud and those that have yet to make that leap. But, experts have pointed out machine learning could be a tool that locks would-be infiltrators out of the cloud, even if the threats they pose are entirely new.

Many of today's cybersecurity interfaces only recognize known threats, but it's possible machine learning could soon understand any suspicious activity that's not necessarily attached to the dangers cybersecurity teams already know.

Legacy cybersecurity tools are typically reactive, but bringing the power of machine learning to the crowd could make them proactive instead.

Changes Are Ahead

This overview clarifies some of the reasons why machine learning will undoubtedly shake up the cloud in the months and years ahead - both in good and bad ways.

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About the Author

Kayla Matthews is a tech-loving blogger who writes and edits ProductivityBytes.com. Follow her on Twitter @productibytes to read all of her latest posts!
Published Wednesday, January 02, 2019 8:01 AM by David Marshall
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