Explore FreshBooks' journey from manual ML model productionization to advanced MLOps maturity in this 30-minute conference talk from the Toronto Machine Learning Series. Learn about the challenges faced by a hybrid team of Data Scientists, ML Engineers, and Data Ops Engineers when developing an ML platform. Gain insights into end-to-end Kubeflow pipelines and a declarative MLOps framework designed to accelerate, simplify, and enhance the reliability of ML pipelines at every stage from development to production. Discover valuable lessons learned and future plans as shared by FreshBooks' lead data scientist, machine learning engineer, and senior data engineers.
Fully Automated ML Platform Using Kubeflow and Declarative Approach to End-to-End ML Development
Toronto Machine Learning Series (TMLS) via YouTube
Overview
Syllabus
Fully Automated ML Platform Using Kubeflow and Declarative Approach to Development of End-to-End ML
Taught by
Toronto Machine Learning Series (TMLS)
