From Models to Systems: Moxank Patel on Building Machine Learning That Works at Scale

My journey into machine learning was not a straight line. It started with a broader foundation in computer engineering and software development, and gradually moved toward solving problems where machine learning could make a measurable difference. 

I completed my bachelor’s degree in Computer Engineering at Dharmsinh Desai University and later pursued a master’s degree in Computer Engineering at San Jose State University. During that period, I became increasingly interested in problems involving large amounts of information and how software could process that information more intelligently. 

One early example was DocFinder, where I worked on search across more than 100,000 documents. We used a microservice architecture and locality-sensitive hashing to make retrieval more efficient. 

Looking back, that project taught me something that has stayed relevant throughout my career: building a model is only one part of the problem. You also have to think about how information reaches the model, how quickly the system responds and how the technology fits into the larger application. 

That thinking eventually led me toward machine learning and, ultimately, large-scale recommendation systems.