As machine learning continues to address common use cases it is crucial to consider what it takes to operationalize your data into a practical, maintainable solution. This is particularly important in order to predict customer behavior more accurately, make more relevant product recommendations, personalize a treatment, or improve the accuracy of research.

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If you happen to be running multiple clusters, each with a large number of services, you’ll find that it’s rather impractical to use static alerts, such as “number of pods < X” or “ingress requests > Y”, or to simply measure the number of HTTP errors. Values fluctuate for every region, data center, cluster, etc. It’s difficult to manually adjust alerts and, when not done properly, you either get way too many false-positives or you could miss a key event.

There used to be a distinct, technical separation between terms such as AI and machine learning (ML) – but only while these technologies remained largely theoretical. As soon as they became practical in the real world, and then commodifiable into products, the marketers stepped in. Widespread overuse of the terms AI/ML in marketing have managed to thoroughly confuse the meanings of these words.

Software has eaten the world and every company today is a software company. This is because every company today is more and more serving its customers digitally. That service can be a spectrum, such as offering traditional physical products and services through digital channels on one end to offering entirely new digital products on the other end. Regardless of where on the spectrum a company is, it does not change the fact that its primary interface with its customers has become its software.

Machine learning in customer service is used to provide a higher level of convenience for customers and efficiency for support agents. Support-focused tools enabled with machine learning are growing in popularity thanks to their increasing ease-of-use and successful applications across a variety of industries. Gartner predicts that by 2021, 15 percent of customer service interactions will be handled completely by artificial intelligence.

Gone are the days when artificial intelligence (AI) was a mere buzzword. Today, AI is transforming our lifestyle. AI enables machines to perform a myriad of tasks with minimal human interference. Whether in sales, marketing, or customer service, AI has revamped business processes. According to a recent Gartner report1, 55% of established companies have started making investments in AI or have prioritized it for their immediate plan of action.

From The Terminator to Ex Machina, Hollywood has a long history of portraying robots and artificial intelligence as an evil cohort with little regard for human life. But what is more alarming than a robot trying to eliminate the human race? How about one trying to take your job? In many places, the future of work is visually represented by robots, and we’re bombarded by headlines claiming it’s only a matter of time before robots take away our livelihoods.

Artificial Intelligence (AI) has amazing potential to drive customer loyalty. Customer loyalty is a huge priority for most businesses. It’s one of the key facets of customer retention, as it reduces customer churn. Reducing churn is desirable since it typically costs four times more to gain a new customer than to keep hold of an existing customer. You also have a 70% probability of making a sale to an existing customer, versus 20% to a new customer.