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

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: 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 – 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 – What parts of your platform did you choose not to build yourself? 40:16 Qwak.ai – 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 – Advantages and disadvantages   --- 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