Julia Silge

SQL is one of the oldest languages still in daily use for data work, and it is now one of the first things any new AI data product promises to write for you. The promise is often natural language to SQL, and it rests on the appealing assumption that the model is what will make the query correct.

Data scientists spend a lot of time and effort writing SQL, yet most data science environments treat it as an afterthought or a separate workflow, forcing you to switch tools, copy results between windows, and lose context along the way.

Data scientists understand what goes into training a machine learning or statistical model, but bringing that model into a production environment can be daunting.

There are many programming languages that we might choose for scientific computing, and we each bring a complex set of preferences and experiences to such a decision. There are significant barriers to learning about other programming languages outside our comfort zone, and seeing another person or community make a different choice can be baffling.

Maintaining machine learning models in production can be quite different from maintaining general software projects, because of the unique statistical characteristics of ML models.