Drag and Drop Matching Questions

Game-theoretic approach to explain feature contributions

Local surrogate model to explain individual predictions

Ranking features by their impact on model output

Shows marginal effect of a feature on predictions

Measures decrease in performance when feature is shuffled

Convert categorical variable into binary columns

Combine two or more features to capture interactions

Normalize numeric features to a standard range

Group continuous values into discrete intervals

Weight term frequency by inverse document frequency

Production ML pipeline framework by Google

ML toolkit for Kubernetes-based workflows

Unified stream and batch data processing service

Managed Apache Airflow workflow orchestration

Serverless ML pipeline orchestration on Vertex AI

Unstructured object storage for any type of data

NoSQL wide-column database for low-latency, high-throughput

Serverless data warehouse for analytics at scale

Relational database for OLTP workloads

NoSQL document database for mobile/web apps

Measure of false positives in classification

Measure of false negatives in classification

Harmonic mean of precision and recall

Root mean squared error for regression

Cross-entropy loss for probabilistic classification

Area Under the ROC Curve

Mean Squared Error

Tensor Processing Unit

Support Vector Machine

Principal Component Analysis

Adds absolute value of weights to loss, induces sparsity

Adds squared magnitude of weights to loss, prevents overfitting

Randomly drops units during training to prevent co-adaptation

Stops training when validation performance stops improving

Increases training data diversity through transformations

End-to-end ML platform for building, deploying, and managing models

Train high-quality custom ML models with minimal effort

Managed service for distributed training of ML models

Custom ASIC for accelerating ML training workloads

Create and execute ML models using SQL queries

Stochastic gradient descent with constant learning rate

Adaptive moment estimation with per-parameter learning rates

Root mean square propagation, adapts learning rate per parameter

Adaptive gradient algorithm, reduces learning rate for frequent features

Accelerates SGD by adding a fraction of previous update

Continuous integration and deployment for ML pipelines

Track and manage different model iterations

Monitor for changes in data or model performance over time

Schedule or trigger model retraining based on conditions

Compare model versions in production with traffic splitting