Module · MLOps Engineer
Python
The working language of ML engineering.
TOPIC 01
Environments & Dependencies
Pin your dependencies and separate code from data before the first training run.
What you'll practice
- Pin the Project Dependencies — guided · beginner
- First Training Script — practice · beginner
A command you'll meet
echo scikit-learn==1.5.0 > requirements.txtThe > redirect writes output into a file — creating it if needed, overwriting it if not.
It is how you will lay down every config and script in these labs. Open any task below
for the full step-by-step walkthrough.
Hands-On Tasks in This Topic (2)
Pin the Project Dependencies
Set up ~/ml-project with a pinned dependency file, a data directory and a dataset manifest — the small ritual that makes every future experiment reproducible. Every command is explained in the walkthrough.
First Training Script
Create a minimal training script and metrics file in ~/train-lab, then lock the work in with your first commit — version control is not just for web apps.
TOPIC 02
Data Wrangling with Pandas
Load the data, then clean it - before a single line of model code runs.
What you'll practice
- Write a Reproducible Loader — guided · beginner
- Clean a Frame Before Training — practice · intermediate
A command you'll meet
cat ~/ml/data/load.pyMost model bugs are data bugs. Open the task below for the full walkthrough.
Hands-On Tasks in This Topic (2)
Write a Reproducible Loader
The loader is the boundary between raw data and your model. Write it so the path, the columns and the row count are all recorded.
Clean a Frame Before Training
Nulls, duplicates and wrong dtypes quietly destroy a model. Write the cleaning step that handles all three.