Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team // Piero Molino // MLOps Coffee Sessions #101

MLOps Coffee Sessions #101 with Piero Molino, Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team, co-hosted by Vishnu Rachakonda.

// Abstract
Declarative Machine Learning Systems are the next step in the evolution of Machine Learning infrastructure.

With such systems, organizations can marry the flexibility of low-level APIs with the simplicity of AutoML.
Companies adopting such systems can increase the speed of machine learning development, reaching the quality and scalability that only big tech companies could achieve until now, without the need for a team of several thousand people.

Predibase is the turnkey solution for adopting declarative ML systems at an enterprise scale.

// Bio
Piero Molino is CEO and co-founder of Predibase, a company redefining ML tooling. Most recently, he has been a Staff Research Scientist at Stanford University, working on Machine Learning systems and algorithms in Prof. Chris Ré's Hazy group. Piero completed a Ph.D. in Question Answering at the University of Bari, Italy. Founded QuestionCube, a startup that built a framework for semantic search and QA. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning, and then joined Geometric Intelligence, where he worked on grounded language understanding.

After Uber acquired Geometric Intelligence, Piero became one of the founding members of Uber AI Labs. At Uber, he worked on research topics including Dialogue Systems, Language Generation, Graph Representation Learning, Computer Vision, Reinforcement Learning, and Meta-Learning. He also worked on several deployed systems like COTA, an ML and NLP model for Customer Support, Dialogue Systems for driver's hands-free dispatch, the Uber Eats Recommender System with graph learning and collusion detection. He is the author of Ludwig, a Linux-Foundation-backed open source declarative deep learning framework.

// MLOps Jobs board  
jobs.mlops.community

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Website: http://w4nderlu.st
http://ludwig.ai https://medium.com/ludwig-ai
Declarative Machine Learning Systems paper by Piero Molino, Christopher Ré: https://cacm.acm.org/magazines/2022/1/257445-declarative-machine-learning-systems/fulltext
Slip of the Keyboard by Sir Terry Pratchett: https://www.terrypratchettbooks.com/books/a-slip-of-the-keyboard/
The Listening Society book series by Hanzi Freinacht: https://www.amazon.com/Listening-Society-Metamodern-Politics-Guides-ebook/dp/B074MKQ4LR

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Piero on LinkedIn: https://www.linkedin.com/in/pieromolino/?locale=en_US

Timestamps:

[00:00] Introduction to Piero Molino

[01:09] Takeaways

[02:52] Blogpost ideas of Demetrios and Vishnu

[03:31] MLOps Swag/Merch

[04:37] What does Predibase do?

[07:40] Valuable paradigm of configuration over code

[10:31] Predibase for ML business outcome

[12:50] Query language to apply and configure models on top of data

[13:17] Query meaning in Predibase

[16:43] Training phase

[19:20] Predibase Pequel System

[20:30] Building Predibase?

[22:52] Perception of one configuration is the right way to do things

[26:10] Predibase edges and limits

[30:09] Strong opinions about Predibase

[32:56] Open-sourcing Ludwig

[35:47] Future of work in the context of Predibase

[40:27] Broadening skill sets

[44:38] Declarative Machine Learning Systems paper

[49:49] Lightning round

[57:26] Predibase is hiring!

[57:49] Wrap up