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What is Machine Learning?

A quick, simple guide that explains the basic concepts behind machine learning.

According to a 2018 Forbes article, over the past four years the number of machine learning patents issued has grown at a Compound Annual Growth Rate (CAGR) of 34%. This places machine learning within the top 3 fastest-growing patent categories.

Professionals in data-prolific industries are in an ideal space for capitalizing on machine learning (ML) technology. In order to do anything with machine learning, we must first understand what machine learning is.

What is Machine Learning?

Machine learning has been defined by academics and industry professionals many times over. At its core, machine learning is a collection of algorithms that use data to make predictions or take actions. It is a form of artificial intelligence (AI) that is focused on enabling a computer system to learn—that is, to be able to optimize its own performance. In machine learning, “learning” is synonymous with an algorithm’s ability to “fine-tune” or “optimize”.

Where does Machine Learning Fit In?

Machine learning is not a singular technique. It is a general approach—a form of applied AI that uses a variety of techniques. These techniques are drawn from the data science discipline and include various ways of organizing and analyzing data: naive Bayes, neural nets, ensembles, and logistic regression are a few of these techniques.

In business applications, machine learning usually forms a part of a general data processing scheme that incorporates aspects of data science (data visualization, distributed architecture, data integration, etc) along with the nitty-gritty mathematical and statistical components of machine learning itself. This whole process is guided by an overall business problem or strategy.

What’s Involved in Machine Learning?

In a practical sense, machine learning involves the similar tools that we already use for programming and data processing. In addition to some kind of computational infrastructure, it requires knowledge of programming (a developer), knowledge of data (data scientist or data engineer), and data.

All of these components are important, but data is often emphasized as the critical component of machine learning—without enough high quality data, solving a problem with machine learning is impossible. This is because, in machine learning, instead of explicitly programming an algorithm to perform a task, a developer needs to provide their algorithm with data (called “training data”) so that the ML system can automatically learn parameters in order to build a model.

That said, while having a supply of data is crucial for solving a problem with ML, the process is far from autonomous—machine learning cannot live on data alone. A human is required at multiple points in the application of machine learning, from identifying the problem to choosing the variables to selecting the best way to visualize data. Although the model will often take certain actions automatically, a human is generally required for making real-world decisions based upon the information that the machine-learned model provides.

What’s the Process for Applying Machine Learning to Business Goals?

Understand the business goals

To start solving real-world business goals with machine learning, we start by identifying and understanding what the organization wants to accomplish. At this stage, we look at the scope of the project to determine whether it can be solved with machine learning. In our practice, we incorporate educational workshops and ideation sessions to establish a common knowledge base and gather critical insight from various industry personnel.

Understand the data

Next, dive into the data. Here, we’re looking for large volumes of high-quality data that can be used to train machine learning models. First, the data is cleaned and put into a structure a data scientist can use to explore the data for patterns and correlations. Throughout this phase, machine learning experts perform reiterative experiments on the data, incorporating an array of techniques.

Development & Integration

Once the experiments have produced a machine learning model that achieves the business goals, the model is built into an application. A common way to do this is through a REST API or front-end application. At this point, developers add necessary features that turn the model into a useful application, considering user-friendly tools, security, and integration.

Evaluation & Support

A robust machine learning application provides both immediate ROI and ongoing value and support. In order to maximize the model’s efficiency and security, it should be regularly evaluated and retrained with new data as necessary.

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