Meetup #93

Building and Using an MLOps Stack with ZenML

Ever wanted to build an MLOps stack that lets you set up a pipeline with scheduled orchestration, drift detection, and automatic metadata tracking for reproducibility? In this talk, Hamza creates a real-world example of a reproducible ML pipeline using ZenML, and showcases how ZenML helps you deploy the pipeline using your favorite tools such as MLFlow, Kubeflow, Evidently, and more.

Take-aways

In this episode

Hamza Tahir

Hamza Tahir

Co-founder, ZenML

Hamza Tahir is a software developer turned ML engineer. An indie hacker by heart, he loves ideating, implementing, and launching data-driven products. His previous projects include PicHance, Scrilys, BudgetML, and you-tldr. Based on his learnings from deploying ML in production for predictive maintenance use-cases in his previous startup, he co-created ZenML, an open-source MLOps framework to create reproducible ML pipelines.

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Demetrios Brinkmann

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

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.