A tree where each path from root to node represents a string prefix -- O(L) lookups where L = string length, independent of number of strings.

Systematic experimentation and iteration: Train candidate algorithms, tune hyperparameters, validate robustly, analyze failures, compare, and select the best model for production.

Garbage in, garbage out: Data quality is the binding constraint on model quality. 60-80% of ML project effort typically goes here, yet it is unsexy and easy to skip.

Count distinct elements in a stream using O(log log n) memory -- with ~2% error rate, vs O(n) for an exact set.

The neural network zoo: Different architectures for different data modalities—images, sequences, and everything in between.

Translating business objectives into a precise ML formulation: Define features (X), targets (y), labeling strategy, cold-start handling, and baseline -- before touching any data at scale.

A tree of hashes where each parent = hash(left child + right child) -- detect exactly which data blocks differ between two systems in O(log n).

Kafka's power comes from partitioned log structure enabling both ordering per partition and massive parallelism; managing consumer offsets is the key complexity.

Starting with WHY, not how: Define the business problem and success metrics before considering ML. Most ML projects fail because they optimize for the wrong objectives, not because algorithms are weak.

Self-balancing tree keeping data sorted with O(log n) reads/writes -- the dominant index structure in relational databases.