Best Practices on English AI Terms Dictionary

Leakage

Sat, 18 Jul 2026 10:04:43 +0000

Definition

Data leakage is a critical error in machine learning where the model gains access to information during training that would not be available at prediction time. This often happens through improper data preprocessing, such as scaling before splitting, or including target-related features in the input set. It results in models that appear highly accurate on validation sets but fail catastrophically in real-world deployment because they rely on impossible-to-obtain data.

Experiment Tracking

Sat, 18 Jul 2026 09:57:38 +0000

Definition

This practice involves logging hyperparameters, dataset versions, model architectures, and performance metrics during training runs. It allows data scientists to compare different experimental configurations, debug issues, and reproduce successful results. Tools like MLflow or Weights & Biases are commonly used to visualize progress and manage the lifecycle of models from development to deployment, ensuring that no critical information is lost between iterations.

Summary

Experiment tracking is the systematic process of recording metadata, metrics, and artifacts from machine learning experiments to ensure reproducibility and facilitate comparison.

Data-centric AI

Sat, 18 Jul 2026 09:52:47 +0000

Definition

Data-centric AI represents a paradigm shift in artificial intelligence development, focusing on systematically improving the data used to train models rather than solely optimizing algorithms or hyperparameters. Proponents argue that high-quality, well-labeled, and diverse datasets yield better performance gains than complex model tweaks. This methodology involves rigorous data auditing, labeling consistency checks, and iterative data refinement. By treating data as a first-class citizen, organizations can build more robust, fair, and accurate AI systems with less computational overhead.