For decades, theoretical computer scientists, mathematicians, and statisticians working in the field of machine learning sorted through a variety of problem settings and modeling techniques. In the past decade, practitioners in the field have converged upon a few well-studied problem settings and modeling techniques. As the theoretical entities under consideration have stabilized, the associated software artifacts have matured, and production systems built from these artifacts have proliferated. Developers possess the skills to make critical improvements in these production machine learning systems.
In this talk, we'll provide a quick introduction to the well-studied problem settings and modeling techniques in machine learning, then discuss how production machine learning systems are built, and finally examine some open source software projects that may be useful when building machine learning systems. Throughout the talk we'll highlight how developers can use their expertise to build better machine learning systems.