Building a Machine Learning model in a Jupyter notebook is easy; keeping it alive, accurate, and monitored in production with real-world streaming data is where 87% of projects die. This is the practical MLOps guide for engineers: the 4 core pillars, modern tool stacks, and how to turn isolated scripts into automated industrial pipelines.
After 60+ articles, 9 technical series, and 4 production applications, this is the real stack I use every day. No sponsorships, no affiliates, no filters. 10 tools organized by workflow phase, what I tried and discarded, and the total cost: less than €5 per month.
Everyone talks about what AI can do. Almost no one talks about what AI costs to do. After more than 6 months operating autonomous agents in production with Gemini, CrewAI and GitHub Actions, I open the books and break down every euro invested — and every surprise I found along the way.
The ‘Organic Traffic Plateau’ is real. To overcome it without losing focus on engineering, I’m launching an extreme ‘dogfooding’ experiment: automating Datalaria’s distribution using Gemini, CrewAI, and GitHub Actions. This is the Master Plan.
We kick off our weather project by building the engine: a Python script that talks to an API, saves historical data, and runs daily thanks to GitHub Actions. I’ll share the tricks and challenges!