No, robots are not taking over the world. However, it's not far fetched to say that they are taking over IT departments across the globe.

The Internet is flooded with stories about AI driving our cars and taking our jobs. Yet all of these potential uses, while incredibly fascinating, fail to mention an important piece of the puzzle--our everyday interactions with artificial intelligence and machine learning technology.

If you work in IT operations, for example, AI is not a few years in the making but rather today’s reality. Machine learning, the type of artificial intelligence that is generally used in IT operations, has little to do with robots but much to do with algorithms. In essence, algorithms are created to analyze various pieces of data and information. The “machine” is then able to detect important patterns based on the algorithms, thus enabling it to make critical contextual decisions.

Supervised vs. Unsupervised Machine Learning

There are two main types of machine learning: supervised machine learning and unsupervised machine learning. In supervised machine learning, the final output is known and the machine has had prior experience with said output.

For example, if a person wants the machine to be able to differentiate a cat from a dog, the outputs (cats and dogs) are known. The machine, which has already learned that animals with larger noses and floppier ears are dogs while the smaller, pointy-eared animal is a cat, is able to use this knowledge to sort a batch of animals into groups of cats and dogs. The entire process is human-led.

In contrast, unsupervised machine learning allows the algorithm to teach itself. There are no training data sets or known outputs, just an initial algorithm that must teach itself. In the same example, the computer is given pictures of cats and dogs. Their algorithm then uses similar characteristics found in each to decipher which group each animal should be a part of. Machines do not know one group is called cats and the other dogs. Instead, they simply focus on the factors they are programmed to analyze.

Many self-driving cars are using this process to learn the rules of the road. However, generally speaking, IT departments use unsupervised machine learning at a far higher rate than supervised machine learning. The reason for this is the sheer complexity of supervised machine learning. While many companies attempt to crack supervised machine learning, most fail. Yet, its value in terms of accuracy along with its ability to filter out noise is unparalleled.

No matter which type of machine learning is implemented, however, there is no question that it is a tremendous asset for IT departments. With the immense amounts of Big Data their machines must analyze as well as the incredibly complex and dynamic environments in which such data exists, machine learning helps to cut through the information for easier clusterization and anomaly detection.

The Complexities of IT Environments

The Big Data within IT environments are diverse sets of information that are produced in incredibly large volumes and must be processed at high speeds. It constantly needs to be analyzed and managed to understand how the machines in an environment are working so that errors and failures can be detected and prevented.

With such data being produced -- often at a rate of terabytes per day -- the task of analyzing all of this information becomes difficult. With the arrivals of microservices, mass cloud migrations, and container infrastructures, the complexities only continue to grow.

Once more and more tools are added to already-complex environments, it becomes even more difficult and time-consuming to analyze all of the resulting data manually. Yet, our machines are constantly talking to us, relaying messages that if ignored, can have a detrimental effect on the health of our systems. When something is a bit “off” in our system, whether it’s due to an error, DDoS attack, or something else entirely, our computers record that information. But sifting through it in order to discover such anomalies is both time consuming and complex. This is where machine learning comes in.

Machine Learning’s IT Revolution

With millions of instances that must be analyzed each day, humans cannot possibly work at the speed that is necessary to go through all of this information. Decisions need to be made quickly -- and the processes by which humans analyze and make decisions is time-consuming and prone to error. This complexity is a problem that stems from Big Data, and machine learning is the solution to that problem.

That’s why CIOs are turning to machine learning technologies to automate and manage their infrastructures and applications. When algorithms evaluate data and quickly respond to problems in IT environments, a lot of time and money is saved.

In addition, AI algorithms can make predictions so that companies will be able to respond to possible occurrences before they even take place. As a result, IT operations departments that take advantage of AI are more productive, efficient, and agile than those who do not.

The Present State of AI in IT

This is only the beginning of the potential that AI can offer IT departments. As the technology continues to grow and becomes increasingly advanced, widespread adoption is inevitable. Soon, there will not be a single IT department that does not use AI in some fashion (unless they want to lag behind).

It is truly a fascinating time to be part of IT’s AI revolution as we look forward to seeing what the technology is set to accomplish next.



Asaf Yigal is the co-founder and VP of Product of the AI-powered log analysis platform Founded in 2014, is the leading provider of enterprise-grade ELK. In addition to Elasticsearch, Logstash, and Kibana’s software features innovative technology such as the machine learning Cognitive InsightsLive Tail  and more. Follow Asaf on Twitter at @asafyigal.