MLOps Weekly Podcast
MLOps Week 2: Uber's Feature Store and Data Quality with Atindriyo Sanyal, Co-founder of Galileo
Jun 14, 2022
Atindriyo Sanyal, Co-founder of Galileo, discusses Uber's feature store and data quality in MLOps. Topics include automation in ML model lifecycle, ideal MLOps workflow, experimentation significance, decentralized vs centralized ML infrastructure comparison, evolution of feature stores, and contrasting data quality tools approaches.
36:23
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Quick takeaways
- MLOps automates ML workflow while providing flexibility and customizability in feature engineering and deployment.
- Experimentation is crucial at different stages of ML lifecycle, focusing on optimizing models for specific use cases.
Deep dives
Applying DevOps principles to ML models
MLOps involves bringing the discipline of DevOps in application development to machine learning models. It applies software engineering principles to automate the lifecycle of ML models, from pre-training and feature engineering to deployment and monitoring.
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