---
title: "Most ML value is still hiding in plain sight"
newsletter: "MLOps Community"
date: 2025-06-12
source: https://aaif.live/newsletters/mlopscommunity/2025-06-12-most-ml-value-is-still-hiding-in-plain-sight
---

# Most ML value is still hiding in plain sight

*Plus -  running reproducible experiment, ML Confessions, list of agent courses, summarising Mary Meeker’s 2025 AI Trends, detecting prompt injection attacks, and Apple’s Foundation Models framework.*

*MLOps Community — Agentic AI Foundation, 2025-06-12*

The event looked great [https://go.mlops.community/d0zjqf], and the t-shirt looked amazing!

If you want to look that good at your next one, pick up a little something here [https://go.mlops.community/m12june].

## Hard-Learned Lessons from Over a Decade in AI

Agents and GenAI are great, but I remember when, back in the day, ML was about helping decisions happen quietly in the background. Funny how that’s still where most of the value is.

I spoke with Mike about why predictive ML still delivers most of the value in production today, especially in automated decisioning systems. We explored how, at Uber, building robust data pipelines - not model serving - was the biggest challenge. That led the Michelangelo team to build an internal feature store, which later inspired the creation of Tecton.

Fraud detection is one area where ML maturity is high. Companies increasingly blend in-house models with external signals to stay ahead:

 * External APIs are used as model inputs
 * Pipelines are tuned for rapid iteration
 * Models are carefully balanced to catch fraud without blocking good users

No ML decision needed - just click below and listen.

Video [https://go.mlops.community/mii6vz] || Spotify [https://go.mlops.community/o5se16] || Apple [https://go.mlops.community/q5vwiw]

[https://go.mlops.community/q5vwiw](https://go.mlops.community/q5vwiw)

[https://go.mlops.community/mii6vz](https://go.mlops.community/mii6vz)

[https://go.mlops.community/o5se16](https://go.mlops.community/o5se16)

## Packaging MLOps Tech Neatly for Engineers and Non-engineers

When I was 8 and no one believed I'd done a backflip over a moving car, I learned a hard lesson about the importance of reproducibility. And an even harder one about road surfaces.

I spoke with Jukka about building an open-source ML platform combining Kubeflow, MLflow, KServe, Prometheus, and Grafana. It’s designed to help researchers and engineers run reproducible experiments and deploy models, even on HPC infrastructure.

We explored how Git-based CI/CD adds much-needed traceability, especially as AI regulations tighten. This setup helps move research beyond isolated experiments:

• Pipelines and deployments can be versioned and automated
• Large-model training runs on HPC clusters
• Teams can more easily bridge the gap from research to production

An easy episode to reproduce - click below to listen again and again.

Video [https://go.mlops.community/7n0nvx] || Spotify [https://go.mlops.community/i9iqxa] || Apple [https://go.mlops.community/46yg9g]

[https://go.mlops.community/46yg9g](https://go.mlops.community/46yg9g)

[https://go.mlops.community/7n0nvx](https://go.mlops.community/7n0nvx)

[https://go.mlops.community/i9iqxa](https://go.mlops.community/i9iqxa)

## Job of the Week

[https://go.mlops.community/ProsusJob](https://go.mlops.community/ProsusJob)

[https://go.mlops.community/MLConfess](https://go.mlops.community/MLConfess)

## Hidden Gems

Most teams buying Blackwell won’t use half of what it can do. Agree?

Hit reply, let me know.

Working on something tricky or planning ahead? Here’s how we can help - just hit reply:

 * Custom workshops tailored to your company’s needs
 * Hiring? I know some quality folks looking for a new adventure
 * Want to connect with someone tackling similar problems? I can introduce you

Thanks for reading, catch you next time!

---
Source: https://aaif.live/newsletters/mlopscommunity/2025-06-12-most-ml-value-is-still-hiding-in-plain-sight