Tell how to use machine learning to simplify workflows.
Machine learning (MO) is one of the most popular topics in computer science that were raised and studied for decades. However, many equate this concept to the next buzzword or even confuse it with the artificial intelligence (AI). But this is not the same thing.
Machine learning is the science of studying and improving machines without special targeted programming. And artificial intelligence is the core technology that makes this possible. We can say that MO is a subset of AI. It is important to remember that all machine learning is an artificial intelligence, not artificial intelligence is machine learning.
Artificial intelligence, machine and deep learning: How are they related?
You also need to understand why recent years machine learning pay so much attention. The development of this discipline contributed to several factors:
- MO is the human face of the digital world. Twitter, for example, uses a sophisticated algorithm to shape your ribbon. This means that two users will not be the same tape, even if they have the same interests and the same subscribers.
- MO continues to progress. When’s the last time I used Alexa, Siri or Cortana? If it’s been a few months, try again. A pleasant surprise awaits you.
- Now access to the MO is open to a huge number of developers. All cloud providers have something to offer in this area, including Google, AWS and Azure.
As MO will help in the business
Regardless of whether it is about understanding the principles of operation of an existing data center or to transfer the on-premises licensing to the cloud, one thing is clear: change is the only constant in IT. If you think about how to measure changes and plan them in sequence, then you should start with the data.
We generate too much data and not enough time to consume them at the same pace, at least without treatment. So how does the company Orient themselves in the information flow until we drown in it? Comes to help MO.
In the last few years is much cheaper as computing power to analyze huge amounts of data, and the means to store them. This means that the ability of MO became available to more users at a much lower cost — life has become much easier. What is missing is a model that can be developed and tested prior to launch.
Filtering of information noise
Let’s look at a practical example of noise. Take the server average CPU utilization — the classic indicator used DevOps.
But characteristics the same server during the day. Focus on Tuesday, as it seems to be the most stressful day.
Tuesday is the busiest day because:
The correct answer is the fourth, all of the above. The fact that people make assumptions, and cars — no. And when you apply the MO to task for understanding what is happening, more like this:
This particular server actually most of the time it works quite a bit. With the exception of Tuesday, Wednesday and Thursday (the actual user traffic is less than 1%). How did we find out? Just filtered data.
Once you remove the excess noise (hint: we used the MO) and look at the actual data level processing (compare the work of your Chrome.exe 20% with just your server, 20%) as well as on network traffic and other variables that separate real activity from the system activity, you can conclude that this server is actually used only three days out of seven.
Once you’ve filtered out the information noise, you will get cleaner data that will allow you to more accurately predict consumer behavior.
The customer segmentation
With the help of machine learning it is possible not only to predict consumer behavior, but also to segment them, for example, to find clients that fit any group.
With the help of machine learning it is possible to divide a huge roster of consumers into segments, each of which is characterized by certain properties. Algorithms will determine what services will be interesting and what group they will respond with higher probability.
You probably noticed that the Internet you come across is, appropriate to your interests and search queries. This is also the handiwork of machine learning.
The algorithms are tracking consumer behavior online, and filter them so that the company could offer the most suitable products for those, who really need it, without distracting with excessive Intrusive advertising and attracting new customers.
And that’s not all, what can the MO. Machine learning is the most effective way to transform all the data collected in the implementation of ideas for a functional business solutions.