MLOps + BI? // Maxime Beauchemin // MLOps Coffee Sessions #104

MLOps Coffee Sessions #104 with the creator of Apache Airflow and Apache Superset Maxime Beauchemin, Future of BI co-hosted by Vishnu Rachakonda. // Abstract // Bio Maxime Beauchemin is the founder and CEO of Preset. Original creator of Apache Superset.  Max has worked at the leading edge of data and analytics his entire career, helping shape the discipline in influential roles at data-dependent companies like Yahoo!, Lyft, Airbnb, Facebook, and Ubisoft. // MLOps Jobs board   https://mlops.pallet.xyz/jobs MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.rungalileo.io/ Trade-Off: Why Some Things Catch On, and Others book by Kevin Maney: https://www.amazon.com/Trade-Off-Some-Things-Catch-Others/dp/0385525958 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Max on LinkedIn: https://www.linkedin.com/in/maximebeauchemin/ Timestamps: [00:00] Introduction to Maxime Beauchemin [01:28] Takeaways [03:42] Paradigm of data warehouse [06:38] Entity-centric data modeling [11:33] Metadata for metadata [14:24] Problem of data organization for a rapidly scaling organization [18:36] Machine Learning tooling as a subset or of its own [22:28] Airflow: The unsung hero of the data scientists [27:15] Analyzing Airflow [30:44] Disrupting the field [34:45] Solutions to the ladder problem of empowering exploratory work and mortals superpowers with data [38:04] What to watch out for when building for data scientists   [41:47] Rapid fire questions [51:12] Wrap up

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Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.