So Fresh and So Data Clean // Tommy Dang // MLOps Meetup #107

MLOps.community · Beginner ·🛡️ AI Safety & Ethics ·3y ago
MLOps Community Meetup #107! Last Wednesday we talked to Tommy Dang, CEO of Mage co-hosted by Ben Epstein. //Abstract Move from your notebook environment to production pipelines by identifying data issues, cleaning them, and exporting a pipeline tracking your changes. // Bio Tommy Dang is the Co-Founder & CEO at Mage. Additionally, Tommy has had 3 past jobs including Head of marketing and promotions platform at Airbnb. // Jobs board https://mlops.pallet.xyz/jobs // Related links ----------- ✌️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, Feature Store, Machine Learning Monitoring, and Blogs: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Ben on LinkedIn: https://www.linkedin.com/in/ben-epstein/ Connect with Tommy on LinkedIn: https://www.linkedin.com/in/dangtommy/ Timestamps: [00:00] Musical introduction to Tommy Dang [03:21] What is Mage? [04:40] Using Docker and Pip [06:30] Cells to map files [07:10] Data loader [11:30] Reports [12:30] Text columns [13:27] Execute a pipeline [14:47] Write reusable encoding block [15:19] Encode binary columns [16:17] Train the model [19:25] Load model and score on test set [20:50] Time to merge! [23:15] Install the dependencies [24:18] Is it a notebook or not? [26:58] Interpretability [30:00] What's next with Mage? [33:04] Dynamic reports [33:57] Variable accessibility [36:29] Lifecycle management [39:10] Different magical block options [42:21] Support for unstructured data [44:21] Unopinionated pipeline creation tool [46:14] Prefect function [47:37] Spark data occurrence [49:15] Wrap up
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