Feature Store
Vertex AI Feature Store is a managed service for storing, serving, and sharing machine learning features consistently across training and serving. PMLE covers it for managing data and models across teams.
All PMLE Terms
Vertex AI
Vertex AI is Google Cloud's unified platform for building, training, deploying, and managing machine learning and generative AI models.
BigQuery ML
BigQuery ML is a capability that lets you create and run machine learning models directly in BigQuery using SQL.
AutoML
AutoML is a set of Google Cloud capabilities that train high-quality models with minimal code by automating architecture and hyperparameter search.
Model Garden
Model Garden is a catalog on the Gemini Enterprise Agent Platform for discovering, testing, and deploying foundational and open models.
Gemini
Gemini is Google's family of multimodal foundational models available on Google Cloud for building generative AI solutions.
Feature Store
Vertex AI Feature Store is a managed service for storing, serving, and sharing machine learning features consistently across training and serving.
Vertex AI Pipelines
Vertex AI Pipelines is a service for orchestrating machine learning workflows as reproducible, containerized steps built on Kubeflow Pipelines.
Kubeflow
Kubeflow is an open-source platform for building portable, scalable machine learning workflows on Kubernetes.
Tensor Processing Unit
A Tensor Processing Unit (TPU) is a Google-designed accelerator optimized for large-scale machine learning training and inference.
Distributed training
Distributed training is the practice of splitting model training across multiple devices or machines to handle large models and datasets.
Online prediction
Online prediction is low-latency, real-time model serving through an endpoint that responds to individual requests.
Batch prediction
Batch prediction is model inference run over a large set of inputs at once, without low-latency requirements.
Training-serving skew
Training-serving skew is a discrepancy between how features are computed during training and during serving, which degrades model performance.
Model drift
Model drift is the degradation of a model's performance over time as the statistical properties of production data change.