Define the new internet.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
2,337 definitions
Borrador de traduccion automatica (Spanish) for "CI Infra Plan": CI Infra Plan is a devops change preview that shows expected infrastructure changes before apply for continuous integration workflows. It uses resource graphs, policy checks, and cost notes so teams can review platform changes safely while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The DevOps team used CI Infra Plan when a pull request entered the build queue, so the team could review platform changes safely before the deployment window opened.”
Borrador de traduccion automatica (Spanish) for "Environment Incident Timeline": Environment Incident Timeline is a devops response record that orders alerts, actions, and decisions during an incident for configuration for a runtime stage. It uses timestamps, owners, and evidence links so teams can learn from outages without guesswork while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The DevOps team used Environment Incident Timeline when staging and production drifted, so the team could learn from outages without guesswork before the deployment window opened.”
Borrador de traduccion automatica (Spanish) for "CD Rollback Plan": CD Rollback Plan is a devops recovery plan that defines how to return to a known good version for deployment automation and promotion. It uses version pins, database notes, and operator steps so teams can recover quickly from bad changes while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The DevOps team used CD Rollback Plan when the release moved toward production, so the team could recover quickly from bad changes before the deployment window opened.”
Borrador de traduccion automatica (Spanish) for "Artifact Infra Plan": Artifact Infra Plan is a devops change preview that shows expected infrastructure changes before apply for build output and package delivery. It uses resource graphs, policy checks, and cost notes so teams can review platform changes safely while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The DevOps team used Artifact Infra Plan when the container image was signed, so the team could review platform changes safely before the deployment window opened.”
Borrador de traduccion automatica (Spanish) for "Pipeline Feature Store": Pipeline Feature Store is a ml service that serves consistent features to training and inference for automated data and model workflow. 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.
“Ejemplo en borrador: The machine learning team used Pipeline Feature Store when the pipeline missed a validation step, so the team could avoid training-serving skew before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) for "Metric Embedding Refresh": Metric Embedding Refresh is a ml index workflow that updates vector representations after source data changes for measurement of model behavior. 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.
“Ejemplo en borrador: The machine learning team used Metric Embedding Refresh when the metric changed after data cleanup, so the team could keep retrieval results current before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) for "Rollback Rollback Plan": Rollback Rollback Plan is a devops recovery plan that defines how to return to a known good version for recovery from a bad deployment. It uses version pins, database notes, and operator steps so teams can recover quickly from bad changes while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The DevOps team used Rollback Rollback Plan when the error budget started burning, so the team could recover quickly from bad changes before the deployment window opened.”
Borrador de traduccion automatica (Spanish) for "Rollback Secret Rotation": Rollback Secret Rotation is a devops credential workflow that replaces sensitive keys without service interruption for recovery from a bad deployment. It uses dual credentials, rollout steps, and revocation so teams can reduce credential exposure while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The DevOps team used Rollback Secret Rotation when the error budget started burning, so the team could reduce credential exposure before the deployment window opened.”
Borrador de traduccion automatica (Spanish) for "Fine-Tuning Training Checkpoint": Fine-Tuning Training Checkpoint is a ml recovery artifact that saves model state during learning for adaptation of a model to a domain. 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.
“Ejemplo en borrador: The machine learning team used Fine-Tuning Training Checkpoint when the fine-tuning run used curated examples, so the team could resume or inspect training safely before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) for "Pipeline Data Split": Pipeline Data Split is a ml experimental control that separates examples for training, validation, and testing for automated data and model workflow. 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.
“Ejemplo en borrador: The machine learning team used Pipeline Data Split when the pipeline missed a validation step, so the team could measure generalization honestly before the model moved into evaluation.”