Integrating the Data Science and App Development Cycles

As data scientists, we are used to developing and training machine learning models in our favorite Python notebook or an integrated development environment (IDE), like Visual Studio Code (VSCode). Often times, any bugs or performance issues go undiscovered until the application has already been deployed. The resulting friction between app developers and data scientists to identify and fix the root cause can be a slow, frustrating, and expensive process. As AI is infused into more business-critical applications, it is increasingly clear that we need to collaborate closely with our app developer colleagues to build and deploy AI-powered applications more efficiently. As data scientists, we are focused on the data science lifecycle, namely data ingestion and preparation, model development, and deployment.