· 8 min · AI/ML, Data Analytics, Career
From Android Dev to ML Pipelines: What My M.Sc. Taught Me the Hard Way
Moving from mobile engineering to AI & Data Analytics at HNU exposed every gap in my mental model. The biggest surprise: the ML part is the easy 20%.
I enrolled at Hochschule Neu-Ulm expecting to learn models. I actually learned data engineering, statistics humility, and why production ML is mostly plumbing.
The 80/20 nobody advertises
Cleaning, joining, and validating data consumed 80% of every project. The model — often a gradient boosted tree that outperformed the fancy architecture — took an afternoon.
Pandas is not a database
My first pipeline loaded 4GB into a DataFrame and died.
- Chunked processing instead of loading the full dataset at once
- Parquet over CSV for anything that gets re-read
- Push filters down to the source query, not after loading
Evaluation is a design problem
Accuracy on an imbalanced dataset is a lie. Choosing the metric — precision vs recall vs calibration — turned out to be a product decision disguised as a technical one.
The best thing I built all semester was an evaluation notebook the whole team could actually read.
Why mobile devs make decent ML engineers
We already think in pipelines (data → transform → render), we already profile before optimizing, and we already distrust the happy path. The syntax is new; the discipline transfers.