Bias and Behavior: The Data Drama You Didn’t Model For — MLOps Community

What better way to spend a birthday than with friends? That’s why I’m inviting you to celebrate mine at openxdata.ai [https://go.mlops.community/OpenNLUTM1] on May 21. Meta, Google, and Netflix have already RSVP'd, though I’m not sure who’s bringing the cake. ## Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data This week’s Meme of the Week, below, felt like the perfect companion to this chat. Devansh took aim at hype-fuelled AI narratives, making the case for structured, workflow-first systems over brittle, autonomous agents. Instead of just training on datasets, he argued for using AI to interrogate and reshape them - embedding intelligence directly into the data layer. Graph-based approaches came up as a promising direction: * Relational design shapes outcomes – the way you model connections changes the insights you get. * Behavior-aware structuring – tracking user actions can surface patterns and friction points early. Well worth a listen - especially if you’re building with agents or thinking about your next meme. Video [https://go.mlops.community/bnkaye] || Spotify [https://go.mlops.community/bv34ys] || Apple [https://go.mlops.community/yp9p4m] [https://go.mlops.community/yp9p4m](https://go.mlops.community/yp9p4m) [https://go.mlops.community/bnkaye](https://go.mlops.community/bnkaye) [https://go.mlops.community/bv34ys](https://go.mlops.community/bv34ys) ## WIN A TICKET TO DATABRICKS DATA + AI SUMMIT Databricks Data + AI Summit [https://go.mlops.community/DBSum] is coming up June 9–12, in San San Francisco and we’ve got a free ticket to give away (worth $1,895). It’s one of the big ones – loads of infra folks, live demos, proper technical talks, and probably a few surprise announcements. If you’re already working with this stack, it’s a useful one to be at. Entering takes less than a minute. Give it a shot [https://go.mlops.community/DBSummitComp] ## Making AI Reliable is the Greatest Challenge of the 2020s Wu-Tang Clan didn’t have the same trouble writing C.R.E.A.M. as Alon and his team did building their copilot-style assistant - what does cash even mean? Turns out, it depends who you ask. Alon shared how disagreements like that make a strong case for treating evals as evolving specs, not fixed benchmarks. Rather than rely on vendor defaults, teams should test each part of the system empirically. He also showed how LLM judges can become scalable stand-ins for expert review, especially when fine-tuned to match domain-specific expectations: * Measure human agreement rates to track judge quality * Regularly prune outdated evals to avoid noise and stay relevant Click below to listen and Protect Ya Neck Build. Video [https://go.mlops.community/gnpcnr] || Spotify [https://go.mlops.community/p5vcey] || Apple [https://go.mlops.community/oc729u] [https://go.mlops.community/oc729u](https://go.mlops.community/oc729u) [https://go.mlops.community/gnpcnr](https://go.mlops.community/gnpcnr) [https://go.mlops.community/p5vcey](https://go.mlops.community/p5vcey) ## Build something smart with real-time web data MCP HACKATHON: AI AGENTS WITH WEB ACCESS Build something smart with real-time web data We’re running a 3-week hackathon with Bright Data: $3,000 for the most useful or creative AI Agent that interacts with the live web. You’ll get access to Bright Data’s MCP (bypasses CAPTCHAs, 403s, and messy sites) and up to $500 in credits to play with. Solo or team, no experience needed. Deadline: June 10. 👉 Join the hackathon [https://go.mlops.community/BDHackathon] and come build something great. ## Job of the Week [https://go.mlops.community/JobUS8May](https://go.mlops.community/JobUS8May) [https://go.mlops.community/MLConfess](https://go.mlops.community/MLConfess) ## QUICK QUESTION In case you missed it on LinkedIn [https://go.mlops.community/pjd8qf], the FastAPI creator launched FastAPI Cloud for single command deployment of FastAPI apps, with autoscaling and HTTPS by default. Could be handy for prototypes, internal tools, or small APIs that don’t need full-blown infra. Given it a spin? Or skipping it? Either way, would love to hear. ## When Prompt Deployment Goes Wrong: MLOps Lessons from ChatGPT’s 'Sycophantic' Rollback We all groan and eye-roll at that overly eager try-hard in daily standups, so it’s unclear why OpenAI wanted ChatGPT to become the corporate version of one. A prompt change in GPT-4o led to ChatGPT dishing out excessive praise. The update rolled out without treating the prompt as a versioned artifact or applying safeguards like canary releases. Short-term engagement metrics gave a misleading signal of success, delaying detection. The blog breaks down how more cautious rollout strategies might have helped: * Shadow deployment: Could have caught the tone shift before users saw it * Canary release: Would have limited exposure to a small group * A/B testing: Might have flagged long-term drops in trust signals like sincerity I just know an absolute legend, sharp-minded wizard of infrastructure, and brilliant beacon of operational excellence like you will click below to read it. Read it here [https://go.mlops.community/33dvrg] [Read it here](https://go.mlops.community/33dvrg) ## Hidden Gems ## HERE TO HELP Before you go, here are three ways I can help - just hit reply: * Curated intros to other community members * What problems are you dealing with? Let me help you find the best solutions through my network * Looking to augment your staff for an MLOps or AI project? I got you covered Thanks for reading, catch you next time!