Nilos Psathas’ Post

Why You Should Break Your ML Pipelines on Purpose. Traditional monitoring won't catch feature drift or data quality issues. Chaos engineering helps you find hidden issues before they cause damage.

Still treating MLOps like an afterthought? That’s a recipe for disaster. Here’s what happens in real teams. You spend weeks tuning a model. You finally hit the metrics you want. It looks great on paper. But push it to production—suddenly, things fall apart. The deployment fails. A data drift sneaks in. No one can debug quickly. No monitoring. No rollbacks. Everyone scrambles. The model sits unused, collecting dust. I’ve seen this story play out too many times. What’s the real problem? It’s never just the code. It’s that the whole lifecycle wasn’t planned from day one. MLOps isn’t just a toolset. It’s the difference between shipping something real or spinning your wheels. It’s about designing for reliability, traceability, and trust—before you write that first line of code. The best AI/ML engineers get this. They build with MLOps in mind right from the start. Ready to stop firefighting and start building real impact?

From Notebook to Production: Shipping LLM Features in 30 DaysMost LLM projects die in the notebook. Here’s how to ship in 30 days—fast, measurable, and low-risk. Day 0: Align - Pick a single user journey and a success metric (e.g., +10% self-serve resolution, p95 < 800ms, <$0.02 per interaction) - Define non-goals and unacceptable failures - Set latency, cost, and SLA budgets Week 1: Data + Evals - Collect/label 200–500 golden examples; scrub PII and deduplicate - Establish a non-LLM baseline to beat - Build an eval harness for quality, hallucination, safety, latency, and cost; define acceptance gates Week 2: Infra + Safety - Choose model(s) and retrieval; keep prompts/configs in version control - Dev/prod parity with tracing (tokens, latency, errors) and cost visibility; secure secrets - Guardrails: input/output filters, jailbreak tests, rate limits, and quotas Week 3: Dry Runs - Offline sweeps and shadow-traffic A/B - Human-in-the-loop review on failures; refine prompts/tools/rules - Define fallbacks and graceful degradation paths Week 4: Ship - Feature flag, canary 1–5%, progressive rollout - Dashboards: p95 latency, cost per session, task success; alerts and on-call playbook - Post-release evals and weekly prompt/config releases Pro tip: De-scope ruthlessly. Speed matters—but risk reduction matters more. If you want the detailed checklist and dashboard template, comment CHECKLIST and I’ll share. #LLM #GenAI #MLOps #AIEngineering #ProductManagement#ArtificialIntelligence #MachineLearning #GenerativeAI #AIAutomation #AITools #LLM #MLOps #AIEngineering #PromptEngineering #AISafety #RAG #OpenAI

Automate to draft, not to damage. Set confidence thresholds; below that, send to a review queue. Log overrides, learn from them, then raise the bar. Speed with brakes is still speed. #aiops #supportops

🚀 "AI Agents: Prototype to Production" is live! Spinning up a demo is easy. The "last mile" to production is where 80% of the engineering happens. We wrote this technical step-by-step guide to bridge that gap! We cover the essential topics you need to reach production: 👥 People & Process: How MLOps, Platform, and Governance teams collaborate to build trust. 🛠️ Quality Gates: Moving to automated trajectory evaluation & CI/CD. 🔄 Ops Loop: Shifting from static monitoring to an "Observe-Act-Evolve" feedback loop. 🤖 Interoperability: Scaling to network of agents with A2A. Huge thanks to my co-authors Dr. Sokratis Kartakis Gabriela Hernández Larios Huang Xia See you today at 11 AM PT for the Livestream! Links in the comments! 👇 Derek Egan Chase Lyall Lavi Nigam Michael Vakoc Yee Sian Ng Polong Lin Michael Clark Salem Haykal Brian Delahunty Kanchana Patlolla Anant Nawalgaria Tiago Henriques Alan Blount

I’ve been implementing multi-agent workflows with LangChain, and one thing is clear: a thoughtful pipeline turns brilliant ideas into dependable products. Here’s the distilled flow I use: 1️⃣ Create LLM — the core intelligence 2️⃣ Write functions — clearly scoped tasks 3️⃣ Decorate with @tool — register callable capabilities 4️⃣ Add metadata/descriptions — help the model choose wisely 5️⃣ Create prompts — define agent identity and limits 6️⃣ Add memory — preserve useful context 7️⃣ Create agent executors — bind LLM + tools + logic 8️⃣ Add router/dispatcher — send queries to the right agent 9️⃣ Add orchestrator — coordinate multi-agent workflows 🔟 Add output parsing — enforce structured, safe outputs 1️⃣1️⃣ Add logging & monitoring — observe, iterate, improve #AI #LangChain #Agents #MLOps #RAG #LLM #ProductionAI

Exactly, with all the vibe coding going around, yes everyone can make a SPA now, maybe some MVPs, but from that to production… we have already seen some atrocities in security or cloud management, the bill will come one way or another. The experience to understand what is wrong in a pool of AI code will not be for the ones that ditched design principles, algorithms, structures and software architecture knowledge, yes we can use AI to speed up coding by 10X maybe at some point even 100x or more, but not everyone will be able to fix the small errors there that may be dependent of an edge case, a business rule that only arises in production, something that comes out of A/B test. And that will cost more than the entire army of agents writing the code. Some will not understand why? But as always time will teach the industry, just let them face the consecuenses of thinking that an AI will fully replace their team. I mean, yes some repetitive and boring tasks should certainly be fully replaced or I would prefer to say automated, if something is predictable, repetitive, and non critical, 100% the AI agent can probably handle it, but I don’t think is to much of a coincidence that Microsoft and Amazon lay off a bunch of people and we see so many issues with the latest windows updates, AWS and azure, there’s certainly a lot of AI code out there today, and probably 80-90% of it works, but is not reliable enough to be production-level. The field moves fast and is certainly improving, but as agentic becomes the rule, finding the issue is becoming harder, if you have a full black box of agents doing everything is hard to find which one needs an instruction updated. Don’t get me wrong, I would love AI to be able to write full production level applications that are ready to handle edge cases and specific business rules already, that would mean we can create competitors for absolutely everything in one go, and for some small industries or businesses it may be happening Already, as I said if your business rule is just a landing with a form, is hard to fail and still we see unencrypted customer lists saved in insecure databases. Right now we are not there and as so many already know vibecoding an app takes you 10m fixing the bugs in that code can take hours, the industry is moving faster but I’m convinced the experience and been able to understand where it makes more sense to change an approach will become increasingly valuable as we move forward. So to the software developers, architects, data scientists, etc. keep learning and improving your skills, keep building that experience belt.

GPT-5.1 powers 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀, 𝗰𝗼𝘀𝘁-𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗔𝗜 🤖. It includes `𝗮𝗽𝗽𝗹𝘆_𝗽𝗮𝘁𝗰𝗵` for precise code edits 🛠️. Also adds a `𝘀𝗵𝗲𝗹𝗹 𝘁𝗼𝗼𝗹` for environment control 🎯. 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝘁𝗼𝗸𝗲𝗻 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 drastically cuts costs 💰. This boosts performance, reducing latency ⚡. Developers report 𝗳𝗮𝘀𝘁𝗲𝗿, 𝘀𝘁𝗲𝗲𝗿𝗮𝗯𝗹𝗲 𝗔𝗜 behavior 🚀. Integrate these 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝘁𝗼𝗼𝗹𝘀 𝗻𝗼𝘄 for a massive advantage 💪. #aineversleeps

Over time, I’ve learned that building strong ML systems isn’t just about better models or faster retraining. It’s about what you feed into them. Most teams focus on model accuracy, GPU time, or automation, and that’s great. But the real test of maturity lies in how seriously you take your data quality. Because when data quietly changes, maybe a column gets renamed, or a value starts meaning something else, your model won’t crash. It will still run, still give outputs, and everyone will assume it’s fine. Until one day, the insights stop making sense. That’s why I believe preventing garbage-in is the most underrated skill in MLOps. It’s not exciting. It’s not flashy. But it protects everything that comes after. The best teams I’ve seen treat data integrity as part of their infrastructure, not as an afterthought. They version every dataset, validate before training, and build alerts that catch issues early. Because once bad data enters the system, no model can fix it; only discipline can. What do you think?

In real environments, the hardest part isn’t building the model, it’s keeping every token, every prompt, every output inside your security boundary. Here’s how teams that actually ship secure, production-ready AI are doing it: 1️⃣ Control Data Exposure Before Anything 2️⃣ Run Models in a Private Boundary 3️⃣ Secure the Prompt & Output Layer 4️⃣ Build RAG With Governance, Not Blind Indexing 5️⃣ Observability & Kill-Switch for Safety At Twendee, we help companies move from concept to production with private LLM architectures, governed data pipelines and compliance-aligned workflows designed for accountability and trust. #ProductionAI #AIprivacy #LLMSecurity