The MLOps Podcast
π€ Feature stores and CI/CD for machine learning with Qwak.ai VP Engineering, Ran Romano
Aug 11, 2021
Ran Romano, VP Engineering at Qwak.ai, discusses the evolution of job titles in machine learning, challenges of using Jupyter notebooks in production, and the importance of adopting a CI/CD approach. They also talk about the challenges in scaling ML models to production, ensuring data reproducibility, and using open source solutions in their ML platform.
45:34
Episode guests
AI Summary
AI Chapters
Episode notes
Podcast summary created with Snipd AI
Quick takeaways
- Effective CI/CD processes are crucial for deploying machine learning models into production and ensuring replicability and versioning of models and data pipelines.
- A feature store plays a vital role in managing reproducibility of training data sets, allowing for easy sharing and reuse of features across different machine learning models.
Deep dives
Building an MLOps Platform
Ron discusses the motivation behind starting the podcast, which is to bridge the gap in knowledge and best practices between different machine learning teams. He believes that the information about bringing machine learning projects into production is not widespread enough, and many people are not familiar with the best practices and approaches. This podcast aims to interview professionals working in various machine learning teams to understand how they are successfully deploying their projects.
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
