Google's seminal paper "Machine Learning: The High-Interest Credit Card of Technical Debt" states that if we look at the whole machine learning system, the actual modeling code is very small. There are a lot of other code around it that configure the system, extract the data/features, test the model performance, manage processes/resources, and serve/deploy the model.
The data component:
Data Storage - How to store the data?
Data Workflows - How to process the data?
Data Labeling - How to label the data?
Data Versioning - How to version the data?
The development component:
Software Engineering - How to choose the proper engineering tools?
Frameworks - How to choose the right deep learning frameworks?
Distributed Training - How to train the models in a distributed fashion?
Resource Management - How to provision and mange distributed GPUs?
Experiment Management - How to manage and store model experiments?
Hyper-parameter Tuning - How to tune model hyper-parameters?
The deployment component
Continuous Integration and Testing - How to not break things as models are updated?
Web - How to deploy models to web services?
Hardware and Mobile - How to deploy models to embedded and mobile systems?
Interchange - How to deploy models across systems?
Monitoring - How to monitor model predictions?
All-In-One: There are solutions that handle all of these components!