⚙️ Machine Learning Fundamentals for Engineers

What you’ll learn

1. The Model Is the Easy PartReframe ML engineering: the model is the small, easy part of a larger data-and-feedback pipeline.In production the model is the smallest, easiest component; the surrounding system — data, features, serving, monitoring — is the real work, as the 'Hidden Technical Debt' paper showed. An ML system is a pipeline, and most failures occur in the seams between stages, especially the training-serving gap.
2. Start From the Decision, Not the AlgorithmTeach problem framing: start from the decision, then define label, inputs, and a baseline.The costliest mistakes precede code. Start from the real decision the prediction drives and the cost of errors; that determines the label, the usable inputs, and the success metric. Define a precise label, use only signals available at prediction time, and set a baseline the model must beat.
3. Your Data Splits Are the ExperimentExplain train/validation/test splits, the sanctity of the test set, and splitting to mimic real querying.Performance claims rest on honest data splits: train to learn, validation to tune, test touched once. Peeking at the test set invalidates it. Split the way the model will be queried — by time for forecasting, by group to avoid the same entity in train and test — and use cross-validation when data is scarce.
4. Data Leakage: The Silent KillerDefine data leakage, its two forms (target leakage, train-test contamination), and how to catch it.Leakage is information in training that won't be available at prediction time or that encodes the answer; it inflates offline metrics and collapses in production. Target leakage uses consequences of the outcome; contamination fits transformations before splitting. Treat suspiciously good results as guilty until proven innocent.
5. Features, and the Skew That Kills ThemExplain feature engineering's importance and training/serving skew, with the feature-store remedy.For most tabular problems, feature quality beats model choice. But features create train/serve skew: a feature computed differently in training and live serving feeds the model unfamiliar inputs, causing silent decay. The remedy is one definition per feature, reused in both training and serving (the feature-store idea).
6. Evaluating HonestlyShow why accuracy misleads on imbalanced data and how to choose a cost-matched metric via precision/recall.On imbalanced problems accuracy hides failure — a majority-class guess can score 99.9% and catch nothing. Break errors into a confusion matrix and use precision (of flagged, how many real) and recall (of real, how many caught), which trade off. Choose the metric by the real cost of false positives vs false negatives, before training.
7. Shipping It, and Watching It RotExplain deployment, silent model decay via data and concept drift, and the need for monitoring.A deployed model is a perishable snapshot of a moving world. Data drift (inputs shift) and concept drift (the input-outcome relationship changes) erode accuracy silently — no exception is raised. Monitoring the input distribution, predictions, and real outcomes is the essential final stage that catches decay before users do.
8. The Loop That Never EndsFrame ML as a continuous MLOps loop and warn about self-reinforcing feedback loops.The pipeline is a loop, not a line: framing, data, features, training, deployment, and monitoring that feeds retraining — the essence of MLOps. Some loops close unintentionally: a model influences the data it later trains on, entrenching bias. The deliverable is the machine that keeps producing good models, and the engineer owns its effect on the world.

Questions this course answers

Grounded in trusted sources

• Sculley et al., Hidden Technical Debt in Machine Learning Systems (NIPS 2015): https://papers.nips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html
• Rules of Machine Learning (best practices) — Google: https://developers.google.com/machine-learning/guides/rules-of-ml
• Leakage (machine learning) — Wikipedia: https://en.wikipedia.org/wiki/Leakage_(machine_learning)
• Precision and recall — Wikipedia: https://en.wikipedia.org/wiki/Precision_and_recall
• Concept drift — Wikipedia: https://en.wikipedia.org/wiki/Concept_drift
• MLOps — Wikipedia: https://en.wikipedia.org/wiki/MLOps

Every Wunder lesson is built from real, reputable sources — never invented.

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