What I Got Wrong About Machine Learning
When I first got into ML, I thought it was all about the models.
The architecture. The math. The accuracy.
But over time, here’s what I realized: most of my early assumptions were wrong.
Here are a few of them.
1. “The best model always wins.”
Reality: the best model is the one that’s fast, interpretable, and deployable.
In production, explainability, latency, and reliability often outweigh tiny accuracy gains.
2. “Offline metrics are all you need.”
I used to obsess over precision, recall, AUC…
But models don’t live in a Jupyter notebook — they interact with people, systems, and real-world feedback loops.
Lesson: Track business impact, not just validation loss.
3. “The data will be ready.”
Nope.
Clean, labeled, well-distributed data is a myth.
Most of my time has gone into understanding, cleaning, and engineering the data — not training.
4. “More complexity = better results.”
Wrong again.
Sometimes a simple heuristic beats a deep model.
The best engineers I’ve worked with know when not to reach for a neural net.
5. “ML is about models.”
This one stung.
ML in the real world is about systems:
- How it’s deployed
- How it’s monitored
- How it impacts users
- How quickly it can be iterated
If you ignore the system, you’re just solving toy problems.
Final Thought
If you’re early in your ML journey, I hope this post saves you time.
And if you’ve been doing this a while, I’d love to hear — what did you get wrong?
Reply, share, or drop a comment. Let’s learn from each other.