Results for "out-of-sample performance"

Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.

Increasing performance via more data.

Guaranteed response times.

Control that remains stable under model uncertainty.

Stored compute or algorithms enabling rapid jumps.

Tradeoff between safety and performance.

Updating a pretrained model’s weights on task-specific data to improve performance or adapt style/behavior.

A mismatch between training and deployment data distributions that can degrade model performance.

When a model fits noise/idiosyncrasies of training data and performs poorly on unseen data.

Fraction of correct predictions; can be misleading on imbalanced datasets.

Plots true positive rate vs false positive rate across thresholds; summarizes separability.

Scalar summary of ROC; measures ranking ability, not calibration.

Average of squared residuals; common regression objective.

Halting training when validation performance stops improving to reduce overfitting.

Standardized documentation describing intended use, performance, limitations, data, and ethical considerations.

Practices for operationalizing ML: versioning, CI/CD, monitoring, retraining, and reliable production management.

Systematic review of model/data processes to ensure performance, fairness, security, and policy compliance.

Logging hyperparameters, code versions, data snapshots, and results to reproduce and compare experiments.

A broader capability to infer internal system state from telemetry, crucial for AI services and agents.

Hardware resources used for training/inference; constrained by memory bandwidth, FLOPs, and parallelism.

Exponential of average negative log-likelihood; lower means better predictive fit, not necessarily better utility.

A dataset + metric suite for comparing models; can be gamed or misaligned with real-world goals.

System for running consistent evaluations across tasks, versions, prompts, and model settings.

Maliciously inserting or altering training data to implant backdoors or degrade performance.

Capabilities that appear only beyond certain model sizes.

Assigning category labels to images.

End-to-end process for model training.

Running new model alongside production without user impact.

Increasing model capacity via compute.

Scaling law optimizing compute vs data.