Can You Make Production ML Work Without Dozens of PhDs?

Machine learning is making it possible to give machines capabilities that were unthinkable just a few years ago. But the techniques for implementing, deploying and maintaining machine learning algorithms and software in products are radically different from the tried-and-true techniques that system developers have used for decades. Successful adoption of machine learning requires a different way of thinking about algorithms, data and software—and different sets of skills. Some companies are fortunate enough to have dozens of machine learning and data science PhDs to aid their product development efforts, but most development groups don’t include even one such expert. Can product development groups successfully deploy robust ML capabilities at the edge without the help of expert specialists? What are the key obstacles to doing so? What approaches are proving effective in making practical ML accessible to the broad community of system developers? Join this lively panel discussion to hear perspectives from a panel of seasoned pros who are working at the leading edge of ML system development, tools and techniques.