# MLOps Week 11: The Evolution of DevOps and the Birth of MLOps with Sam Ramji

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Text version: https://stenobird.com/podcast/mlops-weekly/mlops-week-11-the-evolution-of-devops-and-the-birth-of-mlops-with-sam-ramji.md
Podcast: [MLOps Weekly Podcast](https://stenobird.com/podcast/mlops-weekly)
Published: 2022-09-06T17:02:15+00:00
Episode link: https://rss.com/podcasts/mlops-weekly/608155
Audio file: https://content.rss.com/episodes/132586/608155/mlops-weekly/20220906_050929_e92276c8c456c80ce07dba9b4ac548ac.mp3
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Duration seconds: 2550

## Resource

Sam Ramji explores the fundamental shift from computation-based DevOps to the cognition-based challenges of MLOps. He argues that while DevOps focuses on predictable infrastructure, MLOps must manage the 'silent failures' inherent in probabilistic data feeds.

## Highlights
- Main idea: MLOps differs from DevOps because it deals with cognition and probabilistic outcomes rather than just deterministic computation
- Failure mode: The most dangerous MLOps error is a 'silent failure,' where skewed data causes a model to remain highly confident while producing garbage outputs
- Practical takeaway: Successful infrastructure tools like Kubernetes win through superior interfaces and community-driven abstraction rather than just raw engine performance
- Main idea: The core challenge of modern ML is managing the uncertainty of data feeds and the difficulty of establishing a 'gold standard' for truth
- Strategic insight: High-value IP in the cloud era lies in the ability to build opinionated interfaces that reduce cycle time for developers

## Topics

MLOps, DevOps, Kubernetes, Data Observability, Cloud Infrastructure, Machine Learning, Data Engineering, Software Architecture

## Chapters
- 1:00 — The Journey to DataStax: Sam Ramji discusses his background with Google Cloud, Kubernetes, and the transition from managing stateless workloads to stateful data infrastructure.
- 7:35 — The Gap Between DevOps and Data Engineering: An analysis of the friction between efficient DevOps pipelines and the manual, 'unwilling' nature of current data engineering processes.
- 14:05 — Lessons from the Toyota Production System: Connecting the principles of predictability and cycle time reduction in software to the origins of lean manufacturing.
- 20:20 — The Danger of Silent Failures in ML: Why MLOps requires new competencies in math and observability to detect when trusted data feeds begin to drift or fail.
- 23:20 — Probabilistic Systems and Recommender Models: Discussing the lack of a 'perfect' model and the necessity of A/B testing in production environments.
- 32:55 — Why Kubernetes Won: Examining technological path dependency and the importance of developer-centric interfaces in the adoption of cloud-native tools.
- 39:30 — The Future of MLOps Platforms: Comparing the 'engine-based' approach of Snowflake to the 'interface-based' approach of HashiCorp and what it means for the next generation of MLOps tools.

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