Back office ops · Production
Building a production ML system using only Python — educational tutorial on MLOps components
The problem
Learning about production ML systems is hard and getting hands-on experience without industry access is even harder, leaving practitioners without a practical foundation for MLOps components.
Workflow diagram · grounded in source
1
Feature store setup
integration
“The solution below allows us to convert our user data into a dictionary where user_id is the key – so we can retrieve features for users we want to make predictions for quickly on the fly.”
2
Experiment tracking
output
“In this case — our design is simpler. We'll just capture all the information about our experiments in a CSV file that we use to track results.”
3
Automated training pipeline
ai_action
“the training code can be scheduled and automated. In cases like e-commerce, there is already new data coming in and models frequently need to be retrained.”
4
Continuous integration tests
validation
“Continuous integration is the act of actively committing changes to a central repository, and also covers automated tests and builds. Most of these actions are triggered by git commits and pushes to remote repositories like Github. In th…”
5
Model prediction serving
output
“we use a predict function instead of a model server to fetch features for a given user ID and make a prediction.”
6
Prediction latency monitoring
feedback_loop
“we'll create a simple monitor that records the latency of predictions and reports on the mean”
Reported outcome
The tutorial provides a foundational base for learning the basics of production ML system components using only Python code.
Reported stack
pandassklearnpytest
Frequently asked questions
What did this team achieve with this AI workflow?
The tutorial provides a foundational base for learning the basics of production ML system components using only Python code.
What tools did this team use?
pandas, sklearn, pytest.
How is this back office ops AI workflow structured?
Feature store setup → Experiment tracking → Automated training pipeline → Continuous integration tests → Model prediction serving → Prediction latency monitoring.