MLOps Community
+00:00 GMT
Sign in or Join the community to continue

Building an Open Source MLOps Stack with ZenML Part 2

Posted Dec 14, 2022 | Views 824
# MLOps Stack
# Open Source
# ZenML
# zenml.io
Share
speakers
avatar
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.

+ Read More
avatar
Ben Epstein
Founding Software Engineer @ Galileo

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.

+ Read More
SUMMARY

ZenML has been hard at work rebuilding and rearchitecting their entire framework architecture from scratch. Join us to see how Hamza managed to pull this off, what it means to rebuild an MLOps framework from scratch, and what the new ZenML looks like (including a brand-new UI)!

ZenML is a fully open-source MLOps framework that makes the transition from local development to production pipelines as easy as 1 line of code. Deeply extensible and wildly customizable, ZenML can help you integrate your favorite ML tools under a single system.

+ Read More

Watch More

57:42
The Birth and Growth of Spark: An Open Source Success Story
Posted Apr 23, 2023 | Views 6.3K
# Spark
# Open Source
# Databricks
Building Cody, an Open Source AI Coding Assistant
Posted Aug 28, 2023 | Views 793
# Open Source AI
# Cody
# Sourcegraph
Practical MLOps Part 2
Posted Jun 02, 2021 | Views 634
# DevOps
# Machine Learning