The hidden technical debt in Machine Learning Systems is a paper written in 2015 by several Google Engineers and seen as the foundation of modern date MLOps principles. The actual Machine Learning Code is only a small fraction of the overall Machine Learning Operation Pipeline. Understanding MLOps principles is a requirement to productionize and scale Algorithms. Without proper knowledge, most models will fail to go in production.
In this talk we will explain the 9 areas of functionality that make up model deployment and operations.
Trey has over 20 years of experience in Data Analytics & Data Science practice leadership, sales, and delivery. Primarily focusing on implementations of analysis, design, development, enhancements & testing of applications. His experience spans disparate verticals, including finance, oil & gas, medical, pharmaceutical, sports, energy, hospitality, retail, defense, federal & state government, and technology sectors.
He has earned an executive data science certification via Johns Hopkins University, and is currently pursuing his MicroMasters in Statistics and Data Science, through MIT.
Daniel Schafer is a Technology Enthusiast that is helping Enterprise Customers securely deploy Machine Learning models in a scalable way.
He has over 20 Years of experience in the Software industry, Including Big Data, Infrastructure as Code and Business Intelligence.