University of Minnesota
Software Engineering Center

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Donald Sawyer

Photo of Donald Sawyer
Adjunct Faculty Member
Donald is a Sr. Solutions Architect at phData, Inc. where he leads projects building data systems for business intelligence and machine learning. Most of the implementations leverage distributed big data systems with Spark and Hadoop, where observability becomes even more complicated. He is also an instructor at the University of Minnesota in the Computer Science, Data Science, and Software Engineering departments, teaching courses on big data engineering and architecture. Donald has a Master's in Software Engineering from the University of Minnesota, which he combines with industry experience to design and implement quality data engineering solutions.

Recent Presentations

Provenance: What's Happening in your Production Data and ML Systems?

Data is often your company's most valuable asset, yet very few implementations of ETL and machine learning capabilities provide the ability to measure their effectiveness (quality), or their performance. Data and machine learning pipelines are built as multi-step software integrations, but when an issue arises, how will you determine what happened? Machine learning models degrade over time, but without the ability to observe them, your models could be ineffective long before someone notices.

Your Laptop isn't Production: Engineering Your ML Models into Production

Do your ML products run on a data scientist’s laptop? Are they being run manually? Is the “production” version the first one that ran without crashing? Is it extremely challenging to update and redeploy your ML products? ML engineering and data engineering might not be the sexiest part of data science, but without understanding the engineering of building, deploying, optimizing, and monitoring ML systems, you’re handcuffing a model’s full potential.