Abstract: 

There’s a fundamental misunderstanding that plagues many companies on their ML journey. Oftentimes, businesses try to adapt tried-and-true DevOps processes used to write traditional software to execute the unfamiliar task of Machine Learning operationalization (aka MLOps). But a copy/paste approach between DevOps and MLOps rarely works. In this talk, we’ll draw a parallel between developing a library and developing a model — and show why Machine Learning requires a new approach.

Bio: 

Conrado Miranda is co-founder and CTO at Verta, a Palo Alto-based startup building tools for AI & ML model management and operations. Conrado holds a Ph.D. in Machine Learning and has built scalable AI platforms throughout his career. As the technical lead for the Deep Learning platform at Twitter’s Cortex, he designed and led the implementation of TensorFlow for model development and PySpark for data analysis and engineering. He also led efforts on NVIDIA’s self-driving car initiative, including the Machine Learning platform, large-scale inference for the Drive stack, and build & CI for Deep Learning models.