Machine learning and the five vectors of progress

What’s keeping leaders from adopting machine learning? Well, tools are still evolving, practitioners are scarce, and the technology is a bit inscrutable for comfort. But five vectors of progress are making it easier, faster, and cheaper to deploy machine learning and could bring it into the mainstream.

Though nearly every industry is finding applications for machine learning—the artificial intelligence technology that feeds on data to automatically discover patterns and anomalies and make predictions—most companies are not yet taking advantage. However, five vectors of progress are making it easier, faster, and cheaper to deploy machine learning and could eventually help to bring the technology into the mainstream. With barriers to use beginning to fall, every enterprise can begin exploring applications of this transformative technology.

Machine learning is one of the most powerful and versatile information technologies available today. But most companies have not begun to put it to use. One recent survey of 3,100 executives in small, medium, and large companies across 17 countries found that fewer than 10 percent were investing in machine learning.

A number of factors are restraining the adoption of machine learning. Qualified practitioners are in short supply. Tools and frameworks for doing machine learning work are immature and still evolving. It can be difficult, time-consuming, and costly to obtain the large datasets that some machine learning model-development techniques require.

Then there is the black-box problem. Even when machine learning models appear to generate valuable information, many executives seem reluctant to deploy them in production. Why? In part, because their inner workings are inscrutable, and some people are uncomfortable with the idea of running their operations on logic they don’t understand and can’t clearly describe. Others may be constrained by regulations that require businesses to offer explanations for their decisions or to prove that decisions do not discriminate against protected classes of people. In such situations, it’s hard to deploy black-box models, no matter how accurate or useful their outputs.

These barriers are beginning to fall. Deloitte has identified five key vectors of progress that should help foster significantly greater adoption of machine learning in the enterprise. Three of these advancements—automation, data reduction, and training acceleration—make machine learning easier, cheaper, and/or faster. The others—model interpretability and local machine learning—open up applications in new areas.

The five vectors of progress, ordered by breadth of application, with the widest first:

Automating data science. Developing machine learning solutions requires skills from the discipline of data science, an often-misunderstood field practiced by specialists in high demand but short supply. Data science is a mix of art and science—and digital grunt work. The reality is that as much as 80 percent of the work on which data scientists spend their time can be fully or partially automated. This work might include data wrangling—preprocessing and normalizing data, filling in missing values, for instance, or determining whether to interpret the data in a column as a number or a date; exploratory data analysis—seeking to understand the broad characteristics of the data to help formulate hypotheses about it; feature engineering and selection—selecting the variables in the data that are most likely correlated with what the model is supposed to predict; and algorithm selection and evaluation—testing potentially thousands of algorithms in order to choose those that produce the most accurate results.

Automating these tasks can make data scientists not only more productive but more effective.