Argo Workflows
One of the most popular, and useful, ways to productionize a machine learning solution is scheduled batch workflows. In this approach, we deliver predictions in regular intervals. There are many tools available allowing you to construct and schedule your workflows. When there are many options, it can be difficult to choose. In this session, we talk about Argo Workflows for batch workflows; how to build them; and why you may want to adopt them in your MLOps stack.
In this episode
Kemal Tugrul Yesilbek
Senior Machine Learning Engineer, Beat
Kemal is a Senior Machine Learning Engineer at Beat, one of the fastest-growing ride-hailing apps in Latin America. He studied software engineering and machine learning. During his time in academia, he published machine learning solutions approaching human-level performance. Kemal started his career as a data scientist. He founded Elify.io, a skill assessment tool for data-driven roles, which resulted in an exit. He is working as a machine learning engineer for the past years, delivering end-to-end machine learning-backed solutions.
Demetrios Brinkmann
Host
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.
Ben Epstein
Host
Ben was the machine learning lead for Splice Machine, leading the development of their MLOps platform and Feature Store. He is now a founding software engineer at Galileo (rungalileo.io) focused on building data discovery and data quality tooling for machine learning teams. Ben also works as an adjunct professor at Washington University in St. Louis teaching concepts in cloud computing and big data analytics.