# MLOps Week 26: Product and Data Team Collaboration with Chinar Movsisyan

Page: https://stenobird.com/podcast/mlops-weekly/mlops-week-26-product-and-data-team-collaboration-with-chinar-movsisyan
Text version: https://stenobird.com/podcast/mlops-weekly/mlops-week-26-product-and-data-team-collaboration-with-chinar-movsisyan.md
Podcast: [MLOps Weekly Podcast](https://stenobird.com/podcast/mlops-weekly)
Published: 2024-02-15T06:36:27+00:00
Episode link: https://rss.com/podcasts/mlops-weekly/1346784
Audio file: https://content.rss.com/episodes/132586/1346784/mlops-weekly/2024_02_15_06_35_32_019067a7-ea1a-494c-bfc4-dbcb52b20763.mp3
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Duration seconds: 1775

## Resource

Bridging the gap between product requirements and engineering execution is the core challenge of modern AI deployment. This discussion explores how structured feedback loops and data-driven insights can prevent model failure in production.

## Highlights
- Main idea: Model performance is driven more by data quality and representativeness than by increasing parameter counts
- Practical takeaway: Use structured data packets instead of vague bug reports to communicate model failures from PMs to engineers
- Failure mode: Relying on static test sets can create a false sense of security, as they often fail to capture edge cases found in real-world environments
- Main idea: The evolution of MLOps is shifting focus from model architecture to robust data curation and monitoring
- Practical takeaway: Effective collaboration requires tools that allow non-technical stakeholders to visualize and categorize model blind spots

## Topics

MLOps, Computer Vision, Product Management, Data Engineering, Model Monitoring, AI Deployment, Edge AI, Data Curation

## Chapters
- 1:00 — From Academia to Industry: Chinar shares her journey from applied mathematics and research in computer vision to founding a startup focused on real-world AI challenges.
- 5:20 — The ML Lifecycle Gap: An exploration of the friction between building high-performing models in research and maintaining reliability in production environments.
- 7:30 — Defining Acceptance Criteria: The importance of establishing clear, shared metrics for data representativeness and test set validity between stakeholders.
- 9:45 — Translating Feedback into Engineering Tasks: How to move beyond vague 'it doesn't work' complaints by providing engineers with actionable, data-backed insights.
- 14:10 — Data vs. Model Parameters: Why scaling model size is often less effective than addressing gaps in data coverage and identifying model blind spots.
- 22:35 — Edge Computing and Model Distillation: The technical constraints of deploying large foundation models on edge devices and the necessity of custom, distilled models.
- 27:00 — The Future of Computer Vision: Predicting the growth of multi-modal models in industrial sectors like manufacturing, mining, and robotics.

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## Transcript

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