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




Thiago de Faria


DataOps Consultant

Thiago de Faria has organised devopsdays Amsterdam, ITNEXT, He is open source advocate, public speaker, proud father and DataOps Consultant. Excited about the business