Don't Panic! a Scientific Approach to Debugging Production Failure

Your production system just broke down. What should you do now? Can you imagine your shiny application / flask app, or your API service breaking down? As a beginning programmer, or operations (or devops) person it can be overwhelming to deal with logs, messages, metrics and other possible relevant information that is coming at you at such a point. And when something fails you want it to get back to working state as fast as possible. [Read More]

Distributing data science products

Where or what is production? What does it mean when someone says to bring some data science product ‘in production’ ? What does it mean for data science products to be in production? Is your product already in production? Is it a magical place? I think two questions are of importance: does my ‘thing’ provide value? is my work repeatable? If the answer to these questions is yes, than your ‘thing’ is in production. [Read More]

Stability, Portability and Flexibility Trade-offs

I think a lot about moving single R scripts from someone’s computer to the cloud (another computer). One of the major questions you need to answer is: Can I give my solution to someone else in a way that it ‘just’ works? R is an high level language. This allows you to write out the steps you want to take and that the actual implementation is hidden (can you imagine writing all the steps your computer needs to take? [Read More]