Machine Learning in the Wild: Scaling Automation Applications from Prototype to Plant Floor
Prototyping machine learning solutions off-line can now be done quickly and efficiently. But what considerations must be taken to ensure that a quickly created prototype can be effectively deployed on the plant floor, in production? When should simpler modeling techniques be favored over more powerful, but also more complex modeling techniques? What should be considered when choosing to deploy a solution on existing PLCs, versus leveraging a separate processing unit? This talk will walk through these considerations in the context of PepsiCo’s potato chip manufacturing process, and answer how some of these questions and considerations have been handled at PepsiCo. In addition, the presentation will discuss how traditionally non-technology companies face a different set of challenges when starting to build machine learning applications at scale.
Shahmeer Mirza is a Senior Research and Development Engineer at PepsiCo. He is focused on leveraging machine learning to develop next generation automation for Industry 4.0 applications. During his time with PepsiCo he has developed robotics, computer vision, sensor, and soft sensor applications for automation using machine learning. Shahmeer has also worked with the strategy and marketing departments to develop predictive AI applications. He holds a B.S. in Chemical and Biomolecular Engineering from Georgia Tech and is currently pursuing his M.S. in Computer Science at Georgia Tech, as well. In his free time he enjoys traveling, scuba diving, playing guitar, and writing.