{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"neptune","slug":"neptune","name":"Neptune","type":"platform","url":"https://neptune.ai","page_url":"https://unfragile.ai/neptune","categories":["model-training"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"neptune__cap_0","uri":"capability://data.processing.analysis.framework.agnostic.experiment.metadata.logging","name":"framework-agnostic experiment metadata logging","description":"Captures training metrics, hyperparameters, and artifacts across any ML framework (PyTorch, TensorFlow, scikit-learn, XGBoost, etc.) through a unified SDK that intercepts logging calls and serializes them to Neptune's backend. Uses a client-side logger that batches metadata into structured JSON payloads and transmits them asynchronously to avoid blocking training loops, with automatic framework detection and adapter patterns for popular libraries.","intents":["Log training metrics from my custom PyTorch model without framework-specific boilerplate","Track hyperparameters and model artifacts across multiple ML frameworks in a single experiment","Capture system metrics (GPU memory, CPU usage) alongside training metrics automatically"],"best_for":["ML teams using heterogeneous tech stacks (PyTorch + XGBoost + scikit-learn)","Researchers prototyping across multiple frameworks without rewriting logging code","Data scientists who want framework-agnostic experiment tracking without vendor lock-in"],"limitations":["Asynchronous batching can introduce 1-5 second delays in metric visibility for real-time monitoring","Custom framework adapters require manual implementation if using proprietary or niche ML libraries","Metadata serialization overhead scales with number of concurrent experiments (>100 simultaneous runs may see latency)"],"requires":["Python 3.7+ or Node.js 12+ (depending on SDK)","Network connectivity to Neptune cloud or self-hosted instance","API token for authentication"],"input_types":["numeric metrics (float, int)","hyperparameter dictionaries (JSON-serializable)","model artifacts (binary files, pickle, ONNX)","system metrics (CPU, GPU, memory)"],"output_types":["structured experiment metadata (JSON)","time-series metric data","artifact references with versioning"],"categories":["data-processing-analysis","ml-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune__cap_1","uri":"capability://data.processing.analysis.multi.dimensional.experiment.comparison.and.visualization","name":"multi-dimensional experiment comparison and visualization","description":"Provides interactive dashboards that compare experiments across multiple dimensions (metrics, hyperparameters, system resources, artifacts) using a columnar data model that indexes experiments by metadata fields. Supports filtering, sorting, and custom chart generation through a web UI that queries Neptune's backend API, with support for parallel coordinates plots, scatter plots, and heatmaps to identify patterns across high-dimensional experiment spaces.","intents":["Compare 50+ experiments to find which hyperparameter combinations produced the best validation accuracy","Visualize the relationship between learning rate, batch size, and final loss across all my training runs","Identify which experiments used the most GPU memory and correlate that with model performance"],"best_for":["ML teams running hyperparameter sweeps with 10+ experiments per project","Researchers analyzing high-dimensional experiment spaces (5+ hyperparameters)","Teams needing to communicate experiment results to non-technical stakeholders via dashboards"],"limitations":["Comparison performance degrades with >500 experiments in a single view (requires pagination or filtering)","Custom chart definitions are limited to pre-built visualization types; custom D3.js charts require API integration","Real-time comparison updates have 5-10 second latency due to backend aggregation"],"requires":["Web browser with JavaScript support","Experiments logged to Neptune with consistent metric names","Read access to experiment project"],"input_types":["experiment metadata (metrics, hyperparameters, tags)","time-series metric data","system resource logs"],"output_types":["interactive visualizations (HTML/Canvas)","comparison tables (CSV export)","chart images (PNG/SVG)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune__cap_10","uri":"capability://data.processing.analysis.dataset.versioning.and.lineage.tracking.with.data.profiling","name":"dataset versioning and lineage tracking with data profiling","description":"Tracks dataset versions used in experiments with automatic profiling (row counts, column statistics, data types, missing values) and lineage tracking back to data sources. Stores dataset metadata (schema, statistics, sample rows) and enables comparison of datasets across experiments to identify data drift or distribution changes. Integrates with data versioning tools (DVC, Pachyderm) to track external dataset versions.","intents":["Track which dataset version was used to train each model","Detect data drift or distribution changes between experiments","Compare model performance across different dataset versions"],"best_for":["teams with evolving datasets and needing to track data lineage","organizations monitoring for data drift in production models","researchers studying the impact of data quality on model performance"],"limitations":["Dataset profiling requires loading full dataset into memory; not suitable for very large datasets (>100GB)","Lineage tracking only works for datasets logged through Neptune SDK; external datasets require manual metadata entry","Data drift detection is manual; no automated alerting for distribution changes","Integration with external data versioning tools (DVC, Pachyderm) requires custom configuration"],"requires":["Dataset files or references (paths, URLs)","Neptune SDK for logging dataset metadata","Sufficient memory for dataset profiling"],"input_types":["dataset files (CSV, Parquet, HDF5, etc.)","dataset metadata (schema, statistics)","data source references (URLs, database connections)"],"output_types":["dataset profiles with statistics and schema","dataset version history with lineage","data drift reports comparing datasets"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune__cap_11","uri":"capability://tool.use.integration.api.driven.experiment.querying.and.programmatic.access","name":"api-driven experiment querying and programmatic access","description":"Exposes a REST API and Python SDK for programmatic access to all Neptune data (experiments, metrics, artifacts, models) enabling integration with external tools and custom workflows. Supports complex queries (filtering, sorting, aggregation) on experiment metadata and metrics, and enables batch operations (tagging, archiving, deleting) across multiple experiments. API responses are JSON-formatted and support pagination for large result sets.","intents":["Query experiments programmatically to find best models or analyze results","Integrate Neptune data into custom dashboards or reporting tools","Automate batch operations like tagging or archiving experiments"],"best_for":["teams building custom ML workflows and needing programmatic access to experiment data","organizations integrating Neptune with existing analytics or BI tools","developers building custom Neptune extensions or plugins"],"limitations":["API rate limiting (varies by plan) may throttle high-frequency queries","Complex aggregations (e.g., percentiles across many experiments) require client-side computation","API documentation is sparse; many endpoints are undocumented","No GraphQL API; REST API requires multiple calls for complex queries"],"requires":["Neptune API token with appropriate permissions","Python 3.7+ for SDK, or any language for REST API","Network connectivity to Neptune backend"],"input_types":["filter expressions (metric ranges, parameter values, tags)","query parameters (sorting, pagination, field selection)","batch operation specifications"],"output_types":["JSON-formatted experiment metadata and metrics","paginated result sets","operation status and results"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune__cap_2","uri":"capability://memory.knowledge.model.registry.with.versioning.and.lineage.tracking","name":"model registry with versioning and lineage tracking","description":"Centralized repository for trained models with semantic versioning, metadata tagging, and automatic lineage tracking that links models to their source experiments, training code, and data versions. Uses a hierarchical storage model (project → model → version) with immutable version snapshots and supports model promotion workflows (staging → production) with approval gates. Integrates with artifact storage (S3, GCS, Azure Blob) to store model binaries while maintaining metadata in Neptune's database.","intents":["Register my trained PyTorch model with metadata about which experiment produced it and which dataset was used","Promote a model from staging to production with an approval workflow and automatic versioning","Query all models trained in the last month and filter by performance metrics to find the best candidate for deployment"],"best_for":["ML teams with formal model governance and approval workflows","Organizations requiring audit trails linking models to training runs and data versions","Teams deploying multiple model versions and needing to track which version is in production"],"limitations":["Approval workflows are limited to sequential stages (staging → production); complex multi-stage pipelines require custom API integration","Model lineage tracking requires explicit logging of data version and code commit; automatic detection not supported","Artifact storage integration requires pre-configured cloud credentials; no built-in local storage option"],"requires":["Neptune API token with write permissions","Cloud storage credentials (S3, GCS, or Azure Blob) for model artifacts","Model metadata (framework, input schema, performance metrics) provided at registration time"],"input_types":["model artifacts (pickle, ONNX, SavedModel, .pt files)","metadata (version tags, performance metrics, training parameters)","lineage information (experiment ID, data version, code commit)"],"output_types":["model registry entries (JSON metadata)","versioned model references","lineage graphs (experiment → model → deployment)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune__cap_3","uri":"capability://tool.use.integration.real.time.collaborative.experiment.monitoring","name":"real-time collaborative experiment monitoring","description":"Enables multiple team members to view and interact with the same experiment dashboard simultaneously through WebSocket-based real-time updates and shared UI state. Uses operational transformation or CRDT patterns to merge concurrent edits (notes, tags, comparisons) without conflicts, with activity feeds showing who made changes and when. Supports commenting on specific metrics or artifacts with @mentions for async collaboration.","intents":["Watch my colleague's training run in real-time and discuss results in a shared dashboard without switching tools","Leave comments on specific metrics or model artifacts for my team to review asynchronously","See who tagged an experiment and why, with full audit trail of metadata changes"],"best_for":["Distributed ML teams working across time zones who need async collaboration","Research groups where multiple people monitor the same training runs","Organizations with formal code review processes that extend to model artifacts"],"limitations":["Real-time updates require persistent WebSocket connections; network interruptions can cause 10-30 second sync delays","Concurrent edits to experiment metadata use last-write-wins semantics; complex merge conflicts not supported","Comment threads are limited to text; no inline code snippets or rich media attachments"],"requires":["WebSocket support in network (no proxy restrictions)","Team members with Neptune project access","Modern web browser with JavaScript enabled"],"input_types":["experiment metadata (tags, notes, comparisons)","user comments (text, @mentions)","metric data (real-time updates)"],"output_types":["shared dashboard state (JSON)","activity feed (event log)","comment threads (text with metadata)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune__cap_4","uri":"capability://data.processing.analysis.custom.metric.and.artifact.logging.with.schema.validation","name":"custom metric and artifact logging with schema validation","description":"Allows teams to define custom metric schemas (e.g., per-class precision, confusion matrix, custom loss functions) and log them with automatic validation against the schema before transmission. Uses JSON Schema or similar validation framework to enforce data types, ranges, and required fields, preventing malformed data from reaching the backend. Supports nested metrics and structured artifacts (images, tables, audio) with automatic serialization and compression.","intents":["Log per-class precision and recall metrics for my multi-class classification model with automatic validation","Define a custom schema for my domain-specific loss function and ensure all experiments log it consistently","Attach confusion matrices, ROC curves, and audio samples to experiments with automatic compression"],"best_for":["Teams with domain-specific metrics that don't fit standard frameworks","Research groups logging complex structured data (images, tables, audio) alongside metrics","Organizations enforcing data quality standards across all experiments"],"limitations":["Schema validation adds 5-10ms per log call; high-frequency logging (>1000 metrics/sec) may see latency","Custom schemas are project-level; no cross-project schema sharing or versioning","Artifact compression is automatic but not configurable; large artifacts (>100MB) may timeout"],"requires":["Schema definition in JSON Schema or Neptune's custom format","SDK version supporting custom metrics (Python 0.9.0+, Node.js 0.5.0+)","Sufficient storage quota for artifacts"],"input_types":["numeric metrics (float, int, arrays)","structured data (JSON, tables)","artifacts (images, audio, video, binary files)"],"output_types":["validated metric payloads (JSON)","compressed artifacts (binary)","schema validation reports (error logs)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune__cap_5","uri":"capability://search.retrieval.experiment.filtering.and.search.by.metadata.and.metrics","name":"experiment filtering and search by metadata and metrics","description":"Provides a query language and UI for filtering experiments by arbitrary metadata fields (tags, hyperparameters, system metrics, custom fields) and metric ranges, with support for boolean operators and regex patterns. Implements a columnar index on frequently-queried fields (learning_rate, batch_size, accuracy) to enable sub-second filtering across thousands of experiments. Saved filters can be shared with team members and used to create dynamic dashboards.","intents":["Find all experiments where learning_rate > 0.001 AND accuracy > 0.95 to identify the best hyperparameter range","Search for experiments tagged 'production-candidate' that ran on GPU and completed in less than 1 hour","Create a saved filter for 'high-performing models' and share it with my team to use in dashboards"],"best_for":["Teams running large hyperparameter sweeps (100+ experiments) and needing fast filtering","Researchers exploring experiment spaces with complex filtering criteria","Organizations standardizing on saved filters for reproducible experiment selection"],"limitations":["Query performance degrades with >1000 experiments and >10 filter conditions; requires index optimization","Regex patterns on metric values require full table scans; not recommended for real-time filtering","Saved filters are project-level; no cross-project filter sharing"],"requires":["Experiments logged with consistent metadata field names","Web UI or API access to Neptune","Read permissions on experiments"],"input_types":["filter expressions (text query language or UI selections)","metadata field names and values","metric ranges (min/max)"],"output_types":["filtered experiment list (JSON)","saved filter definitions","dynamic dashboard configurations"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune__cap_6","uri":"capability://automation.workflow.integration.with.ci.cd.pipelines.for.automated.experiment.tracking","name":"integration with ci/cd pipelines for automated experiment tracking","description":"Provides GitHub Actions, GitLab CI, and Jenkins plugins that automatically log experiment metadata (code commit, branch, CI job ID) and trigger model training workflows with Neptune tracking enabled. Uses environment variable injection to pass Neptune API tokens and project IDs to training scripts, and supports automatic artifact upload from CI artifacts to Neptune's model registry. Enables tracking of which code version produced which model.","intents":["Automatically log the Git commit hash and branch name with every experiment run in my CI/CD pipeline","Trigger a model training job from my CI pipeline and have results automatically logged to Neptune","Upload trained models from CI artifacts to Neptune's model registry with automatic versioning"],"best_for":["Teams with mature CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins) who want experiment tracking integrated","Organizations requiring audit trails linking models to code commits and CI job IDs","Teams automating model training and deployment workflows"],"limitations":["CI/CD plugins require manual configuration per pipeline; no auto-discovery of training scripts","Environment variable injection can expose API tokens in CI logs if not properly masked","Artifact upload from CI is limited to files in CI artifact storage; streaming uploads not supported"],"requires":["GitHub Actions, GitLab CI, or Jenkins with plugin support","Neptune API token stored as CI secret","Training script that accepts Neptune API token via environment variable"],"input_types":["CI environment variables (commit hash, branch, job ID)","training script outputs (metrics, artifacts)","CI artifacts (model files, logs)"],"output_types":["experiment metadata with CI context (commit, branch, job ID)","model registry entries with versioning","CI job logs linked to experiments"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune__cap_7","uri":"capability://safety.moderation.team.access.control.and.project.level.permissions","name":"team access control and project-level permissions","description":"Implements role-based access control (RBAC) with predefined roles (Owner, Editor, Viewer) and fine-grained permissions for experiments, models, and dashboards. Uses a permission matrix stored in Neptune's database to enforce access rules at the API level, preventing unauthorized users from viewing or modifying experiments. Supports team hierarchies and project-level permission inheritance.","intents":["Grant read-only access to my experiments for stakeholders without allowing them to modify or delete runs","Create a team hierarchy where junior researchers can only view experiments but senior researchers can approve model promotions","Audit who accessed which experiments and when for compliance purposes"],"best_for":["Organizations with formal access control requirements (healthcare, finance, regulated industries)","Teams with mixed roles (researchers, engineers, stakeholders) requiring different permission levels","Companies needing audit trails of who accessed sensitive model information"],"limitations":["RBAC is limited to predefined roles; custom role definitions require API integration","Permission changes take 30-60 seconds to propagate across all API servers","No row-level security; users with project access can see all experiments in the project"],"requires":["Team members with Neptune accounts","Project ownership to manage permissions","Enterprise plan for advanced RBAC features"],"input_types":["user email addresses","role assignments (Owner, Editor, Viewer)","permission matrices"],"output_types":["access control lists (JSON)","audit logs (event stream)","permission enforcement at API level"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune__cap_8","uri":"capability://memory.knowledge.automated.data.versioning.and.experiment.reproducibility","name":"automated data versioning and experiment reproducibility","description":"Captures dataset metadata (name, version, hash, row count) alongside experiment logs, enabling reproducibility by linking experiments to specific data versions. Integrates with data versioning tools (DVC, Pachyderm) to automatically log data lineage, and supports manual data version tagging for teams without automated versioning. Enables querying experiments by data version to understand how model performance changes with different datasets.","intents":["Log which version of my training dataset was used for each experiment to ensure reproducibility","Query all experiments trained on dataset version 2.1 to compare model performance across different hyperparameters","Automatically capture data lineage from DVC and link it to my experiments"],"best_for":["Teams with formal data governance and versioning practices (using DVC, Pachyderm, or similar)","Research groups requiring reproducibility and audit trails linking models to data versions","Organizations analyzing how data quality changes affect model performance"],"limitations":["Automatic data versioning requires pre-configured DVC or Pachyderm integration; manual tagging is error-prone","Data hash computation can add 5-10 seconds for large datasets (>1GB); not recommended for real-time logging","Data lineage tracking is limited to supported tools; custom data pipelines require manual logging"],"requires":["Data versioning tool (DVC, Pachyderm) or manual data version tagging","Dataset metadata (name, version, hash) provided at experiment start","Integration credentials for data versioning tools"],"input_types":["dataset metadata (name, version, hash, row count)","data lineage information (from DVC, Pachyderm)","manual data version tags"],"output_types":["experiment-to-data-version mappings (JSON)","data lineage graphs","reproducibility reports (code + data + model)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune__cap_9","uri":"capability://automation.workflow.experiment.scheduling.and.automated.retraining.workflows","name":"experiment scheduling and automated retraining workflows","description":"Provides a scheduler that triggers experiment runs on a cron schedule or event-based triggers (new data arrival, code commit), with automatic logging of scheduled runs to Neptune. Supports parameterized experiments where hyperparameters are injected at runtime, and enables conditional workflows (e.g., retrain only if new data exceeds threshold). Uses a task queue backend (Celery, Airflow) to execute scheduled jobs with fault tolerance and retry logic.","intents":["Automatically retrain my model every night with the latest data and log results to Neptune","Trigger a hyperparameter sweep when a new code commit is pushed to the main branch","Retrain my model only if new data exceeds 10,000 rows, otherwise skip the run"],"best_for":["Teams with automated retraining pipelines (daily, weekly model updates)","Organizations requiring continuous model monitoring and retraining workflows","Research groups running large hyperparameter sweeps on a schedule"],"limitations":["Scheduling requires external task queue (Celery, Airflow); no built-in scheduler in Neptune","Conditional workflows require custom logic; no declarative workflow language","Scheduled runs are limited to single-machine execution; distributed training requires additional orchestration"],"requires":["Task queue backend (Celery, Airflow, or similar)","Training script that accepts hyperparameters as command-line arguments","Neptune API token for logging scheduled runs"],"input_types":["cron schedules or event triggers","hyperparameter configurations (JSON)","conditional logic (thresholds, data checks)"],"output_types":["scheduled experiment runs (logged to Neptune)","execution logs and status","model artifacts from scheduled runs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune__headline","uri":"capability://model.training.ml.experiment.tracking.and.model.management.platform","name":"ml experiment tracking and model management platform","description":"Neptune is a comprehensive platform for ML teams that facilitates experiment tracking and model management, enhancing team productivity with rich metadata logging and collaboration tools.","intents":["best ML experiment tracking tool","model management platform for machine learning","top tools for ML team collaboration","experiment tracking solutions for data scientists","best platforms for managing ML models"],"best_for":["ML teams","data scientists"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+ or Node.js 12+ (depending on SDK)","Network connectivity to Neptune cloud or self-hosted instance","API token for authentication","Web browser with JavaScript support","Experiments logged to Neptune with consistent metric names","Read access to experiment project","Dataset files or references (paths, URLs)","Neptune SDK for logging dataset metadata","Sufficient memory for dataset profiling","Neptune API token with appropriate permissions"],"failure_modes":["Asynchronous batching can introduce 1-5 second delays in metric visibility for real-time monitoring","Custom framework adapters require manual implementation if using proprietary or niche ML libraries","Metadata serialization overhead scales with number of concurrent experiments (>100 simultaneous runs may see latency)","Comparison performance degrades with >500 experiments in a single view (requires pagination or filtering)","Custom chart definitions are limited to pre-built visualization types; custom D3.js charts require API integration","Real-time comparison updates have 5-10 second latency due to backend aggregation","Dataset profiling requires loading full dataset into memory; not suitable for very large datasets (>100GB)","Lineage tracking only works for datasets logged through Neptune SDK; external datasets require manual metadata entry","Data drift detection is manual; no automated alerting for distribution changes","Integration with external data versioning tools (DVC, Pachyderm) requires custom configuration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.328Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=neptune","compare_url":"https://unfragile.ai/compare?artifact=neptune"}},"signature":"AVesBc1QeQfvCN9ZW5kg2jTg/ts3a1FpAt6fmAwmkn8GGGmYuPpL20MvxKHFYWEgDnm41qRlA+vHDPsrY1iCCg==","signedAt":"2026-06-20T12:21:49.565Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/neptune","artifact":"https://unfragile.ai/neptune","verify":"https://unfragile.ai/api/v1/verify?slug=neptune","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}