I started out as an academically trained statistician, then a machine learning applied scientist, and most recently I architect ML infrastructure and design MLOps workflows. On a typical day, my job is to address the the following challenges:
Data is messy. Users of ML systems are experimentalists. Data pipelines and ML models break. Training-serving skew is always lurking. Legacy code and infrastructure never fail to get in the way.
Here’s my preparatory work: I architect ML infrastructure and design MLOps workflows in my current and previous jobs; I worked at an ML-platform-as-a-service company building a distributed database for massive scale machine learning applications; I co-founded a recommendation system powered retail startup, building the ML models and serving infrastructure, and fusing machine learning with the internal ERP and warehouse management systems.