# MLOps Week 21: The Future of ML Governance and Data Management with Kevin Petrie

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Text version: https://stenobird.com/podcast/mlops-weekly/mlops-week-21-the-future-of-ml-governance-and-data-management-with-kevin-petrie.md
Podcast: [MLOps Weekly Podcast](https://stenobird.com/podcast/mlops-weekly)
Published: 2023-07-25T17:13:55+00:00
Episode link: https://rss.com/podcasts/mlops-weekly/1051657
Audio file: https://content.rss.com/episodes/132586/1051657/mlops-weekly/2023_07_25_17_12_17_d867f2fe-b80a-4c3a-bd4d-39f719934fd9.mp3
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Duration seconds: 1626

## Resource

Enterprises struggle to bridge the five-to-seven-year gap between cutting-edge AI innovation and legacy technical debt. This discussion explores how to navigate hybrid cloud environments, data decentralization, and the rise of small language models.

## Highlights
- Main idea: Enterprise AI success depends on managing the 'long tail' of legacy on-premise data that cannot easily move to the cloud
- Practical takeaway: Instead of forced centralization, focus on creating a common management plane or 'virtual feature store' over decentralized data sets
- Failure mode: Centers of Excellence (CoEs) often fail when they rely on voluntary time and dotted lines rather than dedicated executive commitment
- Trend: The industry is shifting toward Small Language Models (SLMs) with curated, governed inputs to solve specific tactical problems
- Strategy: To drive innovation, start with bite-sized, demonstrable problems and scale through broad guiding principles rather than rigid top-down mandates

## Topics

MLOps, AI Governance, Data Management, Enterprise AI, Small Language Models, Data Mesh, Cloud Migration, Technical Debt

## Chapters
- 1:00 — The Enterprise Innovation Gap: Kevin Petrie discusses the structural delay between vendor innovation and enterprise adoption caused by legacy systems.
- 3:10 — Challenges of Hybrid Environments: The complexities of data gravity, sovereignty requirements, and the difficulty of migrating legacy workloads to the cloud.
- 6:45 — Strategies for Implementing Innovation: How to introduce new technologies into large organizations by starting with small, demonstrable wins.
- 8:45 — Data-Centric AI and Technical Debt: Addressing the reality that AI success is fundamentally a projection of the quality of your underlying data pipelines.
- 12:40 — Navigating Data Decentralization: The shift from cloud consolidation toward data mesh patterns and managing multi-cloud environments.
- 14:50 — Regulatory Compliance and Risk: Managing global regulatory requirements like GDPR while handling diverse enterprise datasets.
- 20:45 — The Rise of Small Language Models: Why the future of enterprise AI lies in smaller, specialized models with governed, high-quality inputs.

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## Transcript

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