Browse A-Z

机器辅助翻译草稿 (Chinese) for "Embargo Step": The Embargo Step is a process stage used in PlatPhorm News editorial operations for embargo work. It clarifies how an article, listing, source, or definition moves from discovery to publication while remaining auditable.

“示例草稿: The editor used the Embargo Step to decide whether the article listing was ready for publication.”

机器辅助翻译草稿 (Chinese) for "Embedding Bias Audit": Embedding Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for vector representation of content or entities. It uses slice metrics, representative data, and reviewer notes so teams can surface fairness risks while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Embedding Bias Audit when the embedding index changed, so the team could surface fairness risks before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding Calibration Curve": Embedding Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for vector representation of content or entities. It uses bucketed predictions, reliability diagrams, and threshold analysis so teams can make confidence scores useful while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Embedding Calibration Curve when the embedding index changed, so the team could make confidence scores useful before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding Data Split": Embedding Data Split is a ml experimental control that separates examples for training, validation, and testing for vector representation of content or entities. It uses randomization rules, leakage checks, and seed tracking so teams can measure generalization honestly while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Embedding Data Split when the embedding index changed, so the team could measure generalization honestly before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding Drift Monitor": Embedding Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for vector representation of content or entities. It uses statistical tests, time windows, and alert thresholds so teams can respond before quality drops while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Embedding Drift Monitor when the embedding index changed, so the team could respond before quality drops before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding Embedding Refresh": Embedding Embedding Refresh is a ml index workflow that updates vector representations after source data changes for vector representation of content or entities. It uses batch jobs, backfills, and index validation so teams can keep retrieval results current while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Embedding Embedding Refresh when the embedding index changed, so the team could keep retrieval results current before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding Evaluation Harness": Embedding Evaluation Harness is a ml test system that runs repeatable checks against model behavior for vector representation of content or entities. It uses fixtures, metrics, thresholds, and regression reports so teams can compare releases with evidence while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Embedding Evaluation Harness when the embedding index changed, so the team could compare releases with evidence before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding Feature Store": Embedding Feature Store is a ml service that serves consistent features to training and inference for vector representation of content or entities. It uses versioned feature definitions, freshness checks, and access policies so teams can avoid training-serving skew while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Embedding Feature Store when the embedding index changed, so the team could avoid training-serving skew before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding Hyperparameter Sweep": Embedding Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for vector representation of content or entities. It uses bounded search spaces, trial tracking, and early stopping so teams can find better configurations while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Embedding Hyperparameter Sweep when the embedding index changed, so the team could find better configurations before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding Label Review": Embedding Label Review is a ml quality workflow that checks annotations for consistency and usefulness for vector representation of content or entities. It uses agreement metrics, reviewer queues, and adjudication so teams can improve supervised learning data while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Embedding Label Review when the embedding index changed, so the team could improve supervised learning data before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding Model Card": Embedding Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for vector representation of content or entities. It uses dataset notes, metric tables, and risk statements so teams can publish model behavior honestly while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Embedding Model Card when the embedding index changed, so the team could publish model behavior honestly before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding Provenance Ledger": Embedding Provenance Ledger is a ml record that tracks where data came from and how it changed for vector representation of content or entities. It uses hashes, source labels, and transformation history so teams can audit model inputs reliably while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Embedding Provenance Ledger when the embedding index changed, so the team could audit model inputs reliably before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding Training Checkpoint": Embedding Training Checkpoint is a ml recovery artifact that saves model state during learning for vector representation of content or entities. It uses weights, optimizer state, and run metadata so teams can resume or inspect training safely while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Embedding Training Checkpoint when the embedding index changed, so the team could resume or inspect training safely before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding space": An embedding space is a latent vector space where items such as words, documents, images, users, or products are represented as numerical coordinates. Items with related meanings or features are positioned near one another, which lets models compare similarity, retrieve neighbors, cluster concepts, and perform operations such as interpolation or vector addition.

“示例草稿: A search system can embed both a question and an article into the same embedding space, then retrieve the article whose vector is closest to the question.”

机器辅助翻译草稿 (Chinese) for "Endpoint Abuse Throttle": Endpoint Abuse Throttle is a security anti-abuse control that slows or blocks suspicious repeated behavior for user device and server protection. It uses rate limits, reputation signals, and challenge steps so teams can protect public access without a login wall while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The security team used Endpoint Abuse Throttle when a workstation reported suspicious activity, so the team could protect public access without a login wall before the risk review began.”

机器辅助翻译草稿 (Chinese) for "Endpoint Attack Surface": Endpoint Attack Surface is a security exposure model that lists reachable systems, actions, and trust boundaries for user device and server protection. It uses asset inventory, route discovery, and permission mapping so teams can prioritize risk reduction while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The security team used Endpoint Attack Surface when a workstation reported suspicious activity, so the team could prioritize risk reduction before the risk review began.”

机器辅助翻译草稿 (Chinese) for "Endpoint Containment Plan": Endpoint Containment Plan is a security response plan that limits damage after a suspected compromise for user device and server protection. It uses isolation steps, credential rotation, and communication paths so teams can reduce attacker dwell time while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The security team used Endpoint Containment Plan when a workstation reported suspicious activity, so the team could reduce attacker dwell time before the risk review began.”

机器辅助翻译草稿 (Chinese) for "Endpoint Data Redaction": Endpoint Data Redaction is a security privacy control that removes sensitive values before data leaves a protected context for user device and server protection. It uses field rules, hashing, and safe logging so teams can share evidence without leaking secrets while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The security team used Endpoint Data Redaction when a workstation reported suspicious activity, so the team could share evidence without leaking secrets before the risk review began.”

机器辅助翻译草稿 (Chinese) for "Endpoint Detection Rule": Endpoint Detection Rule is a security security analytic that matches suspicious behavior or known indicators for user device and server protection. It uses logs, thresholds, signatures, and behavioral context so teams can surface actionable alerts while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The security team used Endpoint Detection Rule when a workstation reported suspicious activity, so the team could surface actionable alerts before the risk review began.”

机器辅助翻译草稿 (Chinese) for "Endpoint Evidence Chain": Endpoint Evidence Chain is a security audit record that preserves how security evidence was collected and handled for user device and server protection. It uses timestamps, hashes, owners, and storage controls so teams can support trustworthy investigation while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The security team used Endpoint Evidence Chain when a workstation reported suspicious activity, so the team could support trustworthy investigation before the risk review began.”