PyWeb January 2025 at Bluevine

I'll present how we implemented our Django settings, by using pydantic, which enables us to validate the configurable values and prevent runtime errors. In addition, we run health checks on some configuration parameters' custom types (like DB connection), to ensure the source availability, and provide the configurations' json schema for validating changes in the config (Can be used by AWS AppConfig).

Ruth is a Senior Python developer,working at Bluevine in the Core Backend team.

Discover how to quickly turn your Python scripts into interactive web apps using Streamlit. This session will cover key features like visualisations, widgets, and deployment, empowering you to create user-friendly interfaces with minimal effort.

Short Outline: DNN with SQL + Python using Built-in SQL Functions

1. Data Preprocessing in SQL:
   • Use SQL's built-in functions (SUM(), AVG(), ROUND(), etc.) to perform calculations directly in the database.
2. Fetch Processed Data in Python:
   • Execute SQL queries with processed results using libraries like pandas and sqlalchemy.
3. Feature Preparation in Python:
   • Split the SQL output into features (X) and targets (y), normalize data, and handle missing values.
4. Build and Train DNN Model:
   • Create a neural network with libraries like TensorFlow/PyTorch.
   • Train using preprocessed features as input.
5. Store Results in SQL:
   • Save predictions or results back into the database for further analysis.

Python is a very flexible language when it comes to variable types, but it allows for setting optional type-annotations.

You can mark all of your variables with types and then Python will promptly disregard your type-annotation.

So what is the value of these type annotation and how can you get to that value?