September 27, 2016
The world of machine learning is quickly developing, and with this expansion comes a growing demand for engineers. Those who answer this call will face a set of challenges that differ considerably from those of a typical software engineer.
“Machine learning engineering is a little bit different,” Apple Senior Director of Machine Learning Gaurav Kapoor said. “First, you think about the problem you are trying to solve. The second step is asking, ‘What data do I need to collect to solve the problem?’ In deterministic programming you don’t have any data collection.”
Machine learning relies heavily upon a foundation of statistical analysis. Entrance into the field is thus facilitated by a strong foundation in math, and many machine learning engineers have backgrounds in statistics in addition to computer science.
Engineers must be able to understand and write algorithms that effectively deal with enormous amounts of data and input, which are expedited with mathematical experience.
David Andrzejek is the Vice President of the Vertical Solutions team for Apigee, a company that creates Application Program Interfaces (APIs) that allow large companies to safely and efficiently manage interactions between thousands of users and servers.
“Companies look to machine learning to understand large amounts of customer data,” Andrzejek said. “So being able to find a way to transcribe that data into something that they can understand is something that employers really look for in a potential employee.”
Machine learning has revolutionized data science because machines can now plow through enormous banks of information to identify patterns at a rate no human could possibly attempt. For large corporations with millions of consumers, this ability to scale is essential.
This scalability can be applied in digital security. Apigee Chief Scientist Joy Thomas works to devise security algorithms using machine learning, specifically for Walgreens’ phone app.
“Between the backend [of Walgreens’ servers] and the app, there is a layer of software that Apigee makes that allows Walgreens to manage all the millions of apps that are running,” Thomas said. “There’s a lot of data that flows through [these systems,] and that data… can be used for insights in terms of improving the performance of the applications, and also… to recommend [Walgreens products] related to the customers’ [needs].”
However, the sensitive data that is carried through these systems is also useful to others: competitors, hackers and cybercriminals. Automated machines send large numbers of messages to the servers to collect data on the company’s sales, guess users’ usernames and passwords and crash the website. As hackers develop ways to make these malicious bots act more human in order to trick the system, Thomas and his colleagues must devise ways to counter their tricks.
“Given a set of samples of data which are labeled as [good and bad], the computer goes through the data and finds out what patterns distinguish the good from the bad, and [builds] up a system that can, given new data, be able to classify it,” Thomas said. “Security is a problem that’s still very difficult because every time you discover something, people on the other side change their behavior to hide themselves from the system.”
Security APIs are pragmatic and highly dynamic, but they are not the only useful application of machine learning. As new machine learning techniques are developed and refined, potential uses for the technology are branching in a multitude of innovative directions. New avenues for data collection are improving the way machines identify patterns in statistical data, and this ability to discover significant trends bears great potential for both the companies that drive the economy as well as the economists who analyze it.
IBM is developing Watson, a machine that uses data analysis and language skills to answer complex questions. Google is working on machines that mimic the way human minds play games and make music, and countless other companies have adapted machine learning to various applications in healthcare, imaging and beyond.
“The future of machine learning is huge,” Andrzejek said. “You see teachers, scientists, mathematicians, statisticians, [who are] all leaving their fields to be in machine learning. Machine learning is constantly evolving with the advent of new technology and so are the careers along with it. While the future is certainly murky, it [is] a very progressive and promising field. The demand is high, the pay is good and the job is ever-evolving. Those with a strong affinity for mathematics and science should definitely look into machine learning as a career.”