Why most ML systems fail in practice?
Wrong assumptions, wrong data decisions, and wrong trade-offs break real-world ML systems. These resources are built to help you make better decisions between theory and production.
How this knowledge is structured
Three complementary formats for different stages of
real-world machine learning work.
Knowledge
Precise answers to precise ML problems
Short, focused explanations to solve specific problems when you are actually building something.
Labs
Learn by making decisions, not by reading
Interactive experiments, distributions, and trade-offs that reveal how ML systems actually behave.
Playbooks
Applied ML by industry and use case
Practical frameworks for designing ML systems in real contexts: healthcare, mining, IoT, and more.
Trusted by practitioners and teams
working on real systems
They rely on FuzzyFrog.AI to move faster from theory to practice.
Now it’s your turn.
María G.
Stress causes in university students
"My biggest challenge was analyzing the data, but with Alan’s support I was able to finish everything on time. I had doubts at first, but I’m glad I went ahead."
Fernando L.
Bridge failure prediction
"I was stuck with my project, but the sessions helped me understand that I first needed to narrow down the scope. After that, Alan guided me through development and training."
William P.
Investment fund forecasting
"The templates helped me get started. They don’t solve the entire project — but they’re a solid foundation and absolutely worth the price."
Carlos R.
ML model deployment on AWS
"I needed to build an MVP. Alan helped me with the deployment and taught me how to maintain it afterward — exactly what I needed."
Which ML decisions silently break
real-world systems?
Get a concise, practical pdf guide delivered to your inbox:
"Common ML Decisions That Break Systems (And How to Avoid Them)"