# MLOps Week 15: The Business Impact of MLOps with Liran Hason

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Text version: https://stenobird.com/podcast/mlops-weekly/mlops-week-15-the-business-impact-of-mlops-with-liran-hason.md
Podcast: [MLOps Weekly Podcast](https://stenobird.com/podcast/mlops-weekly)
Published: 2023-02-07T18:03:58+00:00
Episode link: https://rss.com/podcasts/mlops-weekly/814138
Audio file: https://content.rss.com/episodes/132586/814138/mlops-weekly/2023_02_07_07_20_38_47618b48-236d-410c-b914-fcbb557f8e20.mp3
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Duration seconds: 2004

## Resource

Transitioning from building machine learning models to engineering machine learning products is the key to unlocking business value. This discussion explores how observability and product-centric thinking bridge the gap between technical performance and bottom-line impact.

## Highlights
- Main idea: Successful MLOps requires moving beyond model accuracy to focus on the end-user experience and business outcomes
- Practical takeaway: Implement proxy metrics like data drift to monitor model health during the latency period before ground truth labels are available
- Failure mode: Treating ML as a siloed technical task rather than a software product leads to models that lack measurable business impact
- Emerging trend: The rise of the ML Product Manager (MLPM) to translate complex data science metrics into actionable business KPIs
- Strategic approach: Use observability tools as a 'Mixpanel for AI' to track model usage, predictions, and their direct link to business results

## Topics

MLOps, Model Observability, Machine Learning Product Management, Data Drift, ML Infrastructure, Business Intelligence, Model Monitoring, AI Product Strategy

## Chapters
- 1:00 — The Origin of Model Monitoring: Liran describes the transition from using simple Python cron jobs for infrastructure monitoring to founding a dedicated observability company.
- 3:30 — The MLOps Maturity Gap: Many companies have invested heavily in data and science teams but struggle to realize the actual value of their models in production.
- 6:00 — Platform vs. Best-of-Breed: A debate on whether specialized data science teams should build custom solutions or adopt integrated platforms.
- 8:20 — The Complexity of ML Infrastructure: Exploring the deep technical challenges in feature stores, including time travel, security, and collaborative access.
- 10:50 — Using Proxy Metrics for Drift: How to maintain model oversight using data drift and other indicators while waiting for ground truth labels.
- 15:40 — Defining the ML Product: Shifting the mindset from building isolated models to creating software products that serve specific user goals.
- 18:00 — The Rise of the MLPM: The growing demand for Product Managers who can bridge the gap between technical ML metrics and business requirements.
- 25:45 — Measuring Success with Three Pillars: A framework for setting goals across business metrics, usage patterns, and model performance.

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## Transcript

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