Most ML projects do not fail because they lack a good model. They fail because of overlooked details: data leakage, irreproducible experiments, and models quietly decaying in production. The best practices below focus on those unglamorous details—and they are what separate a polished prototype from a system that can handle real-world demands.
Start with a dumb baseline
Before bringing out the heavy artillery, build the simplest possible version of your predictor. Guess the mean, always predict the majority class, or apply a two-line business rule. It may sound pointless, but this baseline gives you the only thing that matters at the beginning: a real number to beat.
Many people train a complex model, see 87% accuracy, and celebrate. Then they discover that always predicting “no” already produced 86%. Without a baseline, you have no idea whether your model is learning anything or merely memorizing the obvious.
Split your data honestly
Data leakage is the most expensive and silent mistake in ML. It happens when information from the future—or from the test set—leaks into training and inflates your metrics. The model looks brilliant in the notebook and falls apart in production.
The rule is simple to state and easy to break: split your data into training, validation, and test sets before touching the data. Normalization, missing-value imputation, feature selection—everything should be fitted only on the training set and then applied to the other sets. If you have temporal data, split by time, never randomly; using future information to predict the past is cheating. Also watch out for features that only exist after the event you want to predict: they are leakage disguised as a gold mine.
Track your experiments
Without tracking, you will fool yourself. It is guaranteed. After twenty rounds of tweaking hyperparameters, features, and seeds, nobody remembers which combination produced that great result three days ago.
For each experiment, record the data version, code commit, hyperparameters, metrics, and model artifact. You can use MLflow, Weights & Biases, or a stubborn spreadsheet—the tool matters less than the habit. The real test is straightforward: can you reproduce the number you put in the report? If the answer is “I think so,” the answer is no.
Simple models first, always
There is an almost irresistible urge to start with the newest architecture you saw on Twitter. Resist it. Logistic regression, trees, and gradient boosting solve an astonishing number of real-world problems—and they come with advantages that deep learning makes you pay dearly for: they train quickly, are easier to debug, and let you explain to the team why the model made a particular decision.
Complexity is a cost, not a medal. Every additional layer is one more thing that can break, something slower to serve, and another challenge to investigate when the metric drops. Increase complexity only when the simple model has truly reached its limits—and when that happens, you will have a strong baseline to prove that the added complexity was worthwhile.
Production is not the finish line
Putting the model into production is the middle of the journey, not the end. The world changes, user behavior changes, and input data changes—and the model, poor thing, knows nothing about any of it. That is drift, and it slowly erodes your performance without triggering any obvious alarm.
Monitor input distributions and business metrics, not just offline accuracy. Define retraining triggers and have a rollback plan for when a new model performs worse than the old one. A model without monitoring is technical debt that quietly compounds interest.
Know when NOT to use ML
Sometimes the best ML practice is not using ML. If an if statement solves the problem, use the if statement. If a business rule, SQL query, or simple heuristic delivers the result, you have just saved months of data collection, training, deployment, and maintenance for a system that would need to be watched forever.
ML makes sense when the pattern is too complex to write by hand, when you have high-quality data at scale, and when occasional errors are acceptable. It does not make sense when you need deterministic guarantees, when you have no data, or when the problem is ultimately just an if statement dressed up for a formal occasion.
In the end, ML best practices are almost entirely about discipline, not cleverness. Baselines, honest data, tracked experiments, simplicity, and production vigilance may not appear in a paper or impress anyone over coffee—but they are exactly what helps your model survive contact with the real world.