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.
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Timestamps:
00:00 Podcast intro
01:00 Guest intro
01:30 Getting into the world of ML and ML Engineering
02:25 The line between Data Engineer, ML Engineer, and Data Scientist
03:50 The future of data roles β what are the trends?
07:21 The most exciting part about taking ML models into production
09:45 Jupyter notebooks in production (again??)
10:41 Signs that notebook productionization might not work
11:42 Building ML-focused CI/CD systems
15:32 Early days of building out the Wix ML platform
16:22 Signs that you might need to focus on ML infrastructure in your organization, and how to convince other stakeholders.
19:21 What part of the platform that you built are you most proud of?
23:51 Defining a feature store and the training/serving skew
27:24 Onboarding data scientists to using a feature store
33:49 When is it too early to build an ML platform?
35:33 Open source components β What parts of your platform did you choose not to build yourself?
40:16 Qwak.ai β What are you working on currently?
41:07 How do you define an "end-to-end" platform in the case of Qwak
44:25 End-to-end vs. Integrated β Advantages and disadvantages
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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
