🐤 Feature stores and CI/CD for machine learning with Qwak.ai VP Engineering, Ran Romano from The MLOps Podcast

🐤 Feature stores and CI/CD for machine learning with Qwak.ai VP Engineering, Ran Romano

45 minutes Posted Aug 11, 2021 at 9:45 am.

Podcast intro

Guest intro

Getting into the world of ML and ML Engineering

The line between Data Engineer, ML Engineer, and Data Scientist

The future of data roles – what are the trends?

The most exciting part about taking ML models into production

Jupyter notebooks in production (again??)

Signs that notebook productionization might not work

Building ML-focused CI/CD systems

Early days of building out the Wix ML platform

Signs that you might need to focus on ML infrastructure in your organization, and how to convince other stakeholders.

What part of the platform that you built are you most proud of?

Defining a feature store and the training/serving skew

Onboarding data scientists to using a feature store

When is it too early to build an ML platform?

Open source components – What parts of your platform did you choose not to build yourself?

Qwak.ai – What are you working on currently?

How do you define an "end-to-end" platform in the case of Qwak

End-to-end vs. Integrated – Advantages and disadvantages  

0:00

45:34

Download MP3

Share at current time

Show notes

In this episode, I'm speaking with Ran Romano from Qwak.ai. Ran built the ML platform at Wix, and we discuss the various data roles, when organizations should focus on ML infrastructure, solving the hard problems of features stores, and one approach to building an end-to-end ML platform.

Join our Discord community: https://discord.gg/tEYvqxwhah

---

Timestamps:

---

Relevant Links:

- Qwak.ai: https://www.qwak.ai

- Wix ML Platform presentation by Ran: https://www.youtube.com/watch?v=E8839ENL-WY

- https://www.linkedin.com/company/dagshub

- https://www.linkedin.com/company/qwak-ai/

- https://twitter.com/TheRealDAGsHub

- https://twitter.com/DeanPlbn

- https://twitter.com/ranvromano

← See all 35 episodes of The MLOps Podcast