Abstract: 

An ML model on a laptop is just a science project. To generate business value at scale, models need to feed applications, model pipelines, and reporting tools, but getting there isn’t easy. The path to production operationalization—and ROI—involves the automation of very specific deployment and management processes for which standard development tools are not designed.

This talk will touch on the unique challenges machine learning introduces to development organizations and detail the strategic decisions businesses must consider to create efficient processes that unlock real insights and maximize productivity of your data science and DevOps teams.

Topics will include:
- How ML and traditional software development are different
- Why ML projects are stalling out in development
- What a day in the life of a data scientist really looks like
- How to scope a life cycle for efficient ML deployment and management
- How the world's leading companies have automated ML operations

Bio: 

Diego Oppenheimer, founder and CEO of Algorithmia, is an entrepreneur and product developer with an extensive background in all things data. Prior to founding Algorithmia he designed, managed and shipped some of Microsoft’s most used data analysis products including Excel, Power Pivot, SQL Server and Power BI.

Diego holds a Bachelors degree in Information Systems and a Masters degree in Business Intelligence and Data Analytics from Carnegie Mellon University.