The business case for machine learning Posted on : Oct 19 - 2018

Machine Learning (ML) is one of the most researched topics in computer science. It’s been around for decades. However, many people consider it just another buzzword, or even worse, confuse it with Artificial Intelligence (AI). However, the two are not the same.

Machine Learning is the science of a machine learning and improving without being specifically programed to do so. Artificial Intelligence is the base technology that makes this possible. Think of ML as a subset of AI. It’s important to keep in mind that all Machine Learning is Artificial Intelligence but not all Artificial Intelligence is Machine Learning.

More than clarifying the difference between the two, it’s important to understand why Machine Learning has gained so much attention over the past few years.

Several factors have contributed to the growth of this discipline:

1. ML makes digital feel more human. For example, Twitter uses a complex algorithm to create your timeline. This means that no two users will have the same experience — even if they follow the same people or have the same followers.
2. ML keeps getting better. When’s the last time you used Alexa, Siri, or Cortana? If it’s less than a few months ago, try again. You’ll be pleasantly surprised.
3. ML is available to more developers. All cloud providers have offerings in the field, including Google, AWS, and Azure. Check them out!

Movere is helping customers maximize their investment in digital technology by helping to wrangle the massive amounts of data generated every day. Whether it’s understanding what’s running in an existing data center, or how your on-premises licensing translates to a cloud environment, we have found that change is the only constant in IT. When you think about how you measure change, and plan for it in a consistent way, data is the best place to start. Nobody can argue with good data as a predictor of what’s to come. However, we produce too much data and can’t consume it at the same pace; definitely not in its raw form. So how do enterprises leverage data when we all seem to be drowning in it?

This is where ML comes to the rescue

In the past few years, both the computing power required to analyze vast amounts of data, and the storage needed to capture it, has gotten significantly less expensive. This means that the power of ML can be made available to more users, at a much lower cost, reducing the barrier to entry for everyone. What is missing is a model that can be developed, tried, and tested before one starts to benefit.

First Step: De-noise the data

Let’s use a practical example of what noise means. Take the CPU utilization of a server. This is a classic metric that DevOps use all the time. View More