Chaos while deploying ML and making sure AI doesn't hurt your app

AI is such a buzzword, with its futuristic implementations and sophisticated machine learning algorithms (Hello, Deep learning!). We are using ML when we need external data to reach a working product because it would be impossible to solve it with the regular for/if/loops. What are the next steps? Moreover, what about Test, Release and Deployment?

We always value data and call our organizations “data-driven”, but now the impact is even bigger. If you are using a ML component, misused/dirty/problematic data will affect not your internal reports as before… but your application deployment and quality of service.

Let’s discuss some AI implementations stories (its advantages/problems) finding common mistakes and future challenges for such a hyped theme.



Thiago de Faria

Thiago is a proud father, mediocre guitar player, enthusiast traveller, mathematician and Statistician by formation but a Software Engineer by choice - he always loved modelling data, predicting and ...