Modern business applications bring together many strands of development. You’re no doubt most familiar with n-tier applications, building on decades of programming skills and techniques, linking UI to code and to data. They’re familiar and easy to understand. But that all changes when you start to add new technologies and approaches, constructing massively scalable distributed computing platforms that take advantage of large amounts of data and machine learning.
Much of modern machine learning builds on using analytical tools to explore data and develop rules for showing statistically significant outliers. Although specialized neural networks handle complex speech and image recognition, most problems don’t require particularly complex models—especially if you’re using predictive algorithms on streams of data from sensors or other IoT hardware. Even so it’s important to try new algorithms out on realm data before you implement them.
