What i do
After 15 years of applying data science across a variety of different verticals, I've found that the combination of human + machine can be far more effective than either one by itself. I try to facilitate that through my work as a speaker, technologist, and data scientist for early- and late-stage technology companies.
Architecting and building a scalable platform can be a very difficult challenge, particularly in start-up companies where there are constantly trade-offs made to balance the need to ship software with the desire to avoid building up too much technical debt. These are the decisions that I thrive on. I've re-architected a machine learning platform from top to bottom within 6 months in order to simplify the code base, make it more modular, enhance software velocity, and improve scalability.
The world of data science continues to march forward at an unbelievable pace with cutting edge tools and techniques emerging on a constant basis. I enjoy staying abreast of all these developments and leveraging the latest and greatest tools in order to build robust models that are capable of achieving a variety of goals.
Model building is often much less challenging than deploying the models to a production environment, monitoring performance, and preventing model drift. Acquiring the skills to not only build the models but deploy them scalably is a rare capability that I have worked hard to develop. Much like software, the key is to achieve high velocity model building by leveraging robust pipelines that can automate as much as possible of the model building and deployment process. This facilitates continual releases (CICD style) to prevent model drift and facilitate model training in hours (not days or weeks like data scientists typically require).
The builders of machine learning platforms tend not to be public speakers and those who are most comfortable in front of an audience tend not to be technology developers. I try to sit in the middle of that Venn Diagram and I am very fortunate to take the occasional break from architecting machine learning platforms in order to teach executives about the latest and greatest developments in artificial intelligence. I cover a variety of topics and try to incorporate a good mix of: (1) where is the technology now?; (2) where is the technology heading?; (3) how can you prepare your organization for this change?; and (4) what are the ethical implications?