Principle:Arize ai Phoenix Dataset Management
| Knowledge Sources | |
|---|---|
| Domains | AI Observability, Dataset Management, Evaluation Infrastructure |
| Last Updated | 2026-02-14 00:00 GMT |
Overview
Dataset management is the practice of organizing, versioning, and maintaining curated collections of input-output examples that serve as the foundation for reproducible AI evaluation and experimentation.
Description
In the context of AI observability and evaluation, a dataset is a versioned collection of examples, where each example consists of an input, an expected output, and optional metadata. Dataset management encompasses the full lifecycle of these collections: creation from diverse sources (dictionaries, DataFrames, CSV files), retrieval by name or identifier, listing available datasets, and incremental augmentation through appending new examples.
Effective dataset management solves several critical problems in AI development:
- Reproducibility: By versioning datasets, teams can ensure that experiments run against the same data produce comparable results. Each mutation of a dataset (such as appending examples) creates a new version, preserving the complete history of changes.
- Standardization: A well-defined example schema (input, output, metadata) enforces consistency across evaluation workflows. This schema allows datasets to be consumed uniformly by tasks and evaluators regardless of how the data was originally structured.
- Traceability: Dataset examples can be linked back to production traces via span IDs, creating a direct connection between observed system behavior and the evaluation data derived from it.
- Collaboration: Centralized dataset storage on a server enables teams to share evaluation data, build on each other's work, and maintain a single source of truth for evaluation benchmarks.
The dataset management principle also includes support for splits, which allow a single dataset to be logically partitioned into subsets (such as "train", "validation", and "test") for different stages of the evaluation workflow.
Usage
Dataset management should be applied in the following scenarios:
- Building evaluation benchmarks: When constructing golden datasets that represent expected system behavior for regression testing.
- Curating production examples: When selecting representative traces from production traffic to use as evaluation inputs.
- Iterative dataset development: When progressively building datasets by appending new examples as edge cases are discovered.
- Cross-team evaluation: When multiple team members need access to the same evaluation data for consistent experiment results.
- Split-based evaluation: When different portions of a dataset should be evaluated independently (e.g., testing on held-out examples while training on others).
Theoretical Basis
Dataset management in the evaluation context follows the principles of version-controlled data management. The core data model can be expressed as:
Dataset = {
id: UniqueIdentifier,
name: String,
description: Optional[String],
version_id: VersionIdentifier,
examples: List[Example],
metadata: Dict[String, Any],
created_at: Timestamp,
updated_at: Timestamp
}
Example = {
id: UniqueIdentifier,
input: Dict[String, Any],
output: Dict[String, Any],
metadata: Dict[String, Any],
splits: Optional[List[String]],
span_id: Optional[String]
}
The versioning model follows an append-only approach: each mutation (creating a dataset with examples, or appending examples to an existing dataset) produces a new version identifier. Previous versions remain accessible, enabling point-in-time retrieval for experiment reproducibility.
The ingestion pipeline supports two primary data paths:
- JSON upload path: For dictionary and list-based inputs, data is serialized to JSON and uploaded directly. This path is optimized for programmatic dataset construction.
- Tabular upload path: For DataFrame and CSV inputs, data is compressed using gzip and uploaded as a CSV stream. This path is optimized for bulk data ingestion and supports column-to-field mapping via key parameters (input_keys, output_keys, metadata_keys).
The split mechanism partitions examples within a single dataset version, allowing filtered retrieval without requiring separate dataset objects. This follows the standard machine learning convention of maintaining train/validation/test splits within a unified dataset.