Neptune AI

Captures and stores experiment metadata (hyperparameters, metrics, artifacts, environment configs) through SDK instrumentation that logs to a centralized metadata store with immutable versioning. Uses a hierarchical schema supporting nested parameter spaces, metric time-series, and artifact lineage tracking across thousands of concurrent experiments without requiring code refactoring.

Provides a query engine that filters and compares experiments across arbitrary dimensions (hyperparameters, metrics, tags, date ranges) and renders interactive dashboards with scatter plots, parallel coordinates, and heatmaps. Uses columnar indexing on metadata to enable sub-second filtering across millions of metric points and supports custom dashboard templates with drag-and-drop widget composition.

Organizes experiments into team workspaces with role-based access control (RBAC) supporting Owner, Editor, and Viewer roles. Enables fine-grained permissions (e.g., 'can promote models to production' vs. 'can only view experiments'). Supports SSO integration (SAML, OAuth) for enterprise deployments and audit logging of all access and modifications.

Allows users to create custom dashboards by composing widgets (charts, tables, metrics cards) that pull data from experiments. Widgets support dynamic filtering and drill-down to experiment details. Dashboards are shareable via links and can be embedded in external tools via iframes. Supports scheduled dashboard refreshes and email delivery of dashboard snapshots.

Centralized model storage with semantic versioning, stage transitions (staging/production/archived), and full lineage tracking linking models to source experiments, training data versions, and deployment metadata. Implements a state machine for model lifecycle management with audit logging of all stage transitions and supports model comparison by metrics, parameters, and artifact checksums.

Enables team members to view, comment on, and compare experiments with granular permission controls (viewer, editor, admin) at project and experiment level. Implements real-time collaboration features including experiment comments with threading, @mentions, and activity feeds showing who modified what and when, with audit logging of all access and modifications.

Monitors deployed models in production by ingesting live prediction metrics and comparing against baseline experiment metrics to detect performance degradation. Uses statistical anomaly detection (z-score, IQR, moving average) to identify metric drift and triggers configurable alerts via email, webhooks, or Slack when thresholds are breached, with root cause analysis linking degradation to data drift or model staleness.

Provides language-specific SDKs (Python, JavaScript/TypeScript) that integrate with popular ML frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost, Keras) via callbacks and decorators to automatically log metrics, hyperparameters, and artifacts without modifying training code. Implements lazy evaluation and batching to minimize logging overhead and supports both synchronous and asynchronous logging modes.

Orchestrates distributed hyperparameter sweeps by defining search spaces (grid, random, Bayesian) and automatically spawning training jobs across multiple machines with centralized result aggregation. Implements early stopping based on intermediate metrics and supports conditional parameter dependencies, enabling efficient exploration of high-dimensional hyperparameter spaces without manual job management.

Tracks data versions and artifact lineage by capturing dataset metadata (schema, row count, checksums), linking experiments to specific data versions, and enabling reproducibility by pinning training data versions. Implements content-addressable storage with checksums to detect silent data changes and supports querying experiments by data version to identify which models were trained on which datasets.

Exposes all Neptune functionality via REST API and Python SDK, enabling programmatic access to experiments, models, and metrics for custom integrations and automation. Implements pagination, filtering, and sorting on all list endpoints with support for complex queries, and provides webhook support for triggering external actions on experiment events (completion, metric threshold crossed, etc.).

Stores experiment artifacts (model checkpoints, plots, CSVs, logs) in Neptune's cloud storage with content-based deduplication to reduce storage costs. Each artifact is versioned and linked to its source experiment; supports retrieval by experiment ID or artifact name. Integrates with training frameworks to automatically capture checkpoints and logs without explicit code changes.