{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"featureform","slug":"featureform","name":"Featureform","type":"platform","url":"https://www.featureform.com","page_url":"https://unfragile.ai/featureform","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"featureform__cap_0","uri":"capability://data.processing.analysis.declarative.feature.definition.with.infrastructure.as.code.pattern","name":"declarative feature definition with infrastructure-as-code pattern","description":"Allows ML engineers to define features using a Python API inspired by Terraform's declarative syntax, storing feature specifications (transformations, data sources, versioning metadata) in a centralized repository without requiring code deployment to compute infrastructure. Features are defined once and automatically versioned, enabling reproducible feature engineering across training and serving pipelines.","intents":["Define reusable ML features once and version them across multiple models and experiments","Maintain a single source of truth for feature logic shared across teams","Track feature lineage and dependencies to understand data provenance","Reproduce historical feature values for model retraining and debugging"],"best_for":["ML teams building multiple models that share common features","Organizations migrating from ad-hoc feature engineering scripts to centralized management","Data engineers standardizing feature definitions across production pipelines"],"limitations":["Feature definitions are stored in Featureform's proprietary format, creating moderate vendor lock-in","No built-in IDE support or syntax highlighting beyond standard Python editors","Declarative API requires learning Featureform-specific abstractions rather than using raw SQL/Spark"],"requires":["Python 3.7+","Access to underlying compute infrastructure (Databricks, Snowflake, or custom provider)","Basic understanding of feature engineering concepts"],"input_types":["Python code (feature definitions)","SQL transformations","Spark/Pandas DataFrames"],"output_types":["Feature metadata (name, variant, lineage, owner, tags)","Versioned feature specifications","Feature repository artifacts"],"categories":["data-processing-analysis","infrastructure-as-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_1","uri":"capability://tool.use.integration.virtual.feature.store.orchestration.across.heterogeneous.data.infrastructure","name":"virtual feature store orchestration across heterogeneous data infrastructure","description":"Sits as a metadata and orchestration layer on top of existing data systems (Databricks, Snowflake, DynamoDB, MongoDB, Redis, Oracle, SAP, SAS) without requiring data migration or new storage systems. Routes feature requests to the appropriate backend storage system based on feature configuration, handling the complexity of multi-system feature serving transparently to the application layer.","intents":["Use existing data warehouses and databases as feature storage without migrating to a dedicated feature store","Serve features from multiple storage backends (batch from Snowflake, real-time from Redis) in a unified API","Avoid vendor lock-in by keeping data in customer-controlled infrastructure","Reduce operational overhead by not managing additional storage systems"],"best_for":["Organizations with existing investments in Databricks, Snowflake, or other data platforms","Teams wanting feature store benefits without infrastructure migration costs","Enterprises with multi-cloud or hybrid deployments requiring flexible backend support"],"limitations":["Performance depends entirely on underlying storage system latency and throughput","No built-in query optimization across heterogeneous backends","Custom provider integrations limited to Enterprise tier, restricting flexibility for open-source users","Requires maintaining and monitoring multiple storage systems independently"],"requires":["At least one supported data infrastructure system (Databricks, Snowflake, DynamoDB, MongoDB, Oracle, SAP, SAS, or Redis)","Network connectivity between Featureform and all backend systems","Appropriate credentials and permissions for each backend system"],"input_types":["Feature requests (entity IDs, feature names, timestamps)","Configuration specifying which backend stores each feature"],"output_types":["Feature vectors (structured data with feature values)","Metadata about feature source and retrieval method"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_10","uri":"capability://search.retrieval.feature.search.and.discovery.with.metadata.tagging.and.grouping","name":"feature search and discovery with metadata tagging and grouping","description":"Enables searching and discovering features across the organization using metadata tags, feature names, owners, and groups. Provides a searchable feature catalog with rich metadata (description, owner, tags, lineage, usage statistics) helping teams find relevant features for model development and understand feature relationships without manual documentation.","intents":["Find existing features relevant to a new modeling task without duplicating feature engineering","Understand which features are owned by which teams for collaboration","Discover features used in similar models to understand best practices","Browse feature catalog to understand available data assets"],"best_for":["Large organizations with many features and teams","ML teams building multiple models that could share features","Organizations standardizing feature engineering practices"],"limitations":["Search capabilities not detailed; unclear if supporting full-text search, tag-based search, or both","No built-in feature recommendation system; search is manual","Metadata schema not documented; unclear what fields are searchable","Search performance not specified; may be slow with thousands of features","No integration with external data catalogs; feature discovery limited to Featureform-defined features"],"requires":["Features defined in Featureform with metadata (tags, descriptions, owners)","Consistent tagging and naming conventions"],"input_types":["Search queries (feature names, tags, owners)","Feature metadata (tags, descriptions, groups)"],"output_types":["Feature search results with metadata","Feature catalog views","Feature lineage and dependency information"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_11","uri":"capability://automation.workflow.transformation.pipeline.orchestration.with.dependency.management","name":"transformation pipeline orchestration with dependency management","description":"Orchestrates feature transformation pipelines across multiple compute systems (Databricks, Snowflake) with automatic dependency resolution and scheduling. Manages complex DAGs of transformations where downstream features depend on upstream features, handling execution order, error handling, and retry logic without requiring separate workflow orchestration tools.","intents":["Define complex feature engineering pipelines with multiple transformation steps","Automatically resolve and execute features in correct dependency order","Schedule regular feature recomputation without manual orchestration","Handle failures and retries in transformation pipelines"],"best_for":["ML teams with complex feature engineering pipelines","Organizations building features that depend on other features","Teams wanting to avoid separate workflow orchestration tools (Airflow, Prefect)"],"limitations":["Orchestration capabilities not detailed; unclear if supporting conditional execution, parallel execution, or advanced scheduling","Dependency resolution algorithm not documented","Error handling and retry policies not specified","Scheduling granularity not specified; unclear if supporting minute-level or only hourly/daily schedules","No built-in monitoring of pipeline execution; requires external tools for visibility"],"requires":["Compute infrastructure (Databricks, Snowflake, or custom provider)","Feature definitions with transformation logic","Dependency specifications between features"],"input_types":["Feature definitions with transformations","Dependency specifications","Scheduling configuration"],"output_types":["Computed features","Pipeline execution logs","Dependency graphs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_12","uri":"capability://data.processing.analysis.training.set.curation.with.label.management.and.feature.label.alignment","name":"training set curation with label management and feature-label alignment","description":"Manages labels (target variables) as first-class artifacts with versioning and lineage tracking, enabling teams to curate training sets by combining specific feature versions with corresponding labels. Handles label delays, label windows, and feature-label temporal alignment automatically, ensuring training sets are correctly constructed for supervised learning without manual data engineering.","intents":["Manage labels alongside features to ensure training-serving consistency","Handle label delays and label windows in time-series prediction tasks","Curate training sets by selecting specific feature and label versions","Track which labels were used for training specific models"],"best_for":["ML teams building supervised learning models with complex label requirements","Organizations with delayed labels (e.g., fraud labels arriving days after transaction)","Teams requiring strict training-serving consistency"],"limitations":["Label management capabilities not detailed; unclear if supporting multi-class, multi-label, or regression labels","Label delay handling not documented; unclear how to specify and validate label delays","No built-in label quality checks; teams must validate labels externally","Label versioning strategy not specified; unclear how to handle label corrections"],"requires":["Label data with timestamps","Feature definitions with corresponding timestamps","Label delay specifications"],"input_types":["Label data (target values with timestamps)","Label metadata (version, source, quality metrics)","Label delay specifications"],"output_types":["Training sets (entity ID, features, label, timestamp)","Label metadata and lineage","Training set statistics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_13","uri":"capability://tool.use.integration.multi.cloud.deployment.with.kubernetes.and.on.premise.support","name":"multi-cloud deployment with kubernetes and on-premise support","description":"Deploys Featureform across AWS, GCP, Azure, Kubernetes clusters, or on-premise infrastructure without code changes, with configuration-driven deployment targeting different cloud providers and infrastructure types. Enables organizations to run feature stores in their preferred cloud environment or on-premise while maintaining consistent feature definitions and APIs across deployments.","intents":["Deploy feature store in preferred cloud provider without vendor lock-in","Run feature store on-premise for data residency or compliance requirements","Deploy to Kubernetes for containerized infrastructure","Maintain consistent features across multiple cloud environments"],"best_for":["Organizations with multi-cloud strategies","Enterprises with on-premise data centers","Teams using Kubernetes for infrastructure management","Organizations with data residency requirements"],"limitations":["Deployment configuration details not documented; unclear what infrastructure-as-code tools are used","Multi-cloud consistency not guaranteed; feature behavior may differ across clouds","On-premise deployment requires managing Featureform infrastructure; no managed service option","Kubernetes deployment requires container orchestration expertise","Cross-cloud feature serving not supported; features must be replicated manually"],"requires":["Cloud account (AWS, GCP, Azure) or Kubernetes cluster or on-premise infrastructure","Appropriate credentials and permissions for deployment","Infrastructure management tools (Terraform, Helm, etc.)"],"input_types":["Deployment configuration (cloud provider, region, infrastructure type)","Feature definitions"],"output_types":["Deployed Featureform instance","Feature serving endpoints"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_2","uri":"capability://data.processing.analysis.point.in.time.correct.training.set.generation.with.temporal.consistency","name":"point-in-time correct training set generation with temporal consistency","description":"Automatically constructs training datasets by joining features and labels at their correct historical timestamps, preventing data leakage by ensuring features used for training reflect only information available at the time of prediction. Implements temporal alignment logic that handles feature updates, label delays, and feature versioning to guarantee training-serving consistency.","intents":["Generate training sets that prevent look-ahead bias and data leakage","Ensure training data matches the temporal context of production serving","Reproduce historical training sets for model retraining and debugging","Handle complex scenarios with delayed labels and feature updates"],"best_for":["ML teams building time-sensitive models (fraud detection, churn prediction, demand forecasting)","Organizations with strict data governance requirements around training-serving consistency","Teams debugging model performance gaps caused by training-serving skew"],"limitations":["Implementation details of temporal alignment logic not publicly documented, making it difficult to audit correctness","Requires all features and labels to have timestamp metadata; missing timestamps cause failures","Performance scales with historical data volume; large lookback windows may cause slow training set generation","No built-in handling for out-of-order or late-arriving data"],"requires":["Features and labels with timestamp columns","Access to historical feature values (requires feature versioning enabled)","Underlying storage system supporting time-range queries"],"input_types":["Entity IDs (e.g., user IDs, transaction IDs)","Label timestamps","Feature lookback windows","Label delay specifications"],"output_types":["Training datasets (rows of entity ID, features, label, timestamp)","Metadata about feature versions used per row"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_3","uri":"capability://memory.knowledge.automatic.feature.versioning.and.lineage.tracking","name":"automatic feature versioning and lineage tracking","description":"Captures and stores all changes to feature definitions, transformations, and datasets automatically, maintaining a complete audit trail of what changed, when, and by whom. Enables rollback to previous feature versions and tracks data lineage from raw sources through transformations to final features, supporting reproducibility and debugging of model behavior changes.","intents":["Understand why model performance changed by identifying which feature definitions were updated","Rollback to a previous feature version if a new transformation introduces bugs","Audit who modified features and when for compliance and governance","Trace data lineage from raw data sources to final features for data quality investigation"],"best_for":["Regulated industries requiring audit trails (finance, healthcare, insurance)","ML teams with multiple engineers modifying features simultaneously","Organizations debugging model performance regressions"],"limitations":["Audit logs (detailed change tracking) limited to Enterprise tier; open-source has basic versioning only","Lineage tracking limited to features defined in Featureform; external data sources may not be fully tracked","No built-in visualization of lineage graphs; requires external tools for complex dependency analysis","Storage overhead increases with number of feature versions; no automatic cleanup policy mentioned"],"requires":["Feature definitions stored in Featureform repository","Underlying storage system supporting version history (most supported systems do)"],"input_types":["Feature definition changes (Python code updates)","Transformation modifications","Dataset updates"],"output_types":["Version history with timestamps and change metadata","Lineage graphs showing data dependencies","Audit logs (Enterprise tier only)"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_4","uri":"capability://safety.moderation.feature.drift.and.data.quality.monitoring.with.automated.alerting","name":"feature drift and data quality monitoring with automated alerting","description":"Continuously monitors feature distributions for statistical drift (changes in mean, variance, or distribution shape) and data quality issues (missing values, outliers, schema violations), comparing current feature values against historical baselines. Integrates with Slack and PagerDuty to alert teams when drift exceeds configured thresholds, enabling proactive model performance management.","intents":["Detect when feature distributions shift, indicating potential model performance degradation","Identify data quality issues (missing values, invalid types) before they impact models","Set up automated alerts so teams respond to data problems without manual monitoring","Investigate root causes of feature drift by examining historical trends"],"best_for":["ML teams operating models in production where data drift causes performance degradation","Data quality-sensitive applications (fraud detection, credit risk, medical diagnosis)","Organizations with on-call rotations requiring automated incident detection"],"limitations":["Drift detection algorithm details not documented; unclear if using statistical tests (KS test, Wasserstein distance) or simpler heuristics","Baseline calculation method not specified; may not handle seasonal patterns or expected distribution changes","Alerting limited to Slack and PagerDuty; no native integration with other incident management systems","No built-in remediation recommendations; alerts require manual investigation","Monitoring latency not specified; may not catch real-time drift in streaming features"],"requires":["Historical feature data to establish baselines","Slack workspace or PagerDuty account for alerts","Threshold configuration for drift sensitivity"],"input_types":["Feature values (continuous or categorical)","Historical feature distributions","Drift threshold configurations"],"output_types":["Drift alerts (Slack messages, PagerDuty events)","Drift statistics (magnitude, direction, affected features)","Data quality reports"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_5","uri":"capability://automation.workflow.multi.variant.feature.management.with.a.b.testing.support","name":"multi-variant feature management with a/b testing support","description":"Enables defining multiple versions (variants) of the same feature with different transformation logic, allowing teams to experiment with alternative feature engineering approaches without modifying production features. Routes requests to specific variants based on configuration, supporting A/B testing of feature engineering changes and gradual rollout of new feature definitions.","intents":["Test new feature engineering approaches without impacting production models","Run A/B tests comparing model performance with different feature variants","Gradually roll out feature changes by routing a percentage of traffic to new variants","Maintain multiple feature definitions for different use cases or model versions"],"best_for":["ML teams experimenting with feature engineering improvements","Organizations running A/B tests on feature engineering changes","Teams managing multiple models with different feature requirements"],"limitations":["Variant routing logic not documented; unclear if supporting percentage-based routing or deterministic assignment","No built-in statistical testing framework for A/B test analysis","Variant management UI/tooling not described; may require API-only configuration","Performance impact of variant routing not specified"],"requires":["Feature definitions with variant specifications","Routing configuration (which variant to serve to which requests)"],"input_types":["Feature requests with variant identifiers","Variant definitions with alternative transformation logic"],"output_types":["Feature values from selected variant","Metadata indicating which variant was served"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_6","uri":"capability://data.processing.analysis.embedding.management.and.vector.database.integration","name":"embedding management and vector database integration","description":"Provides native support for storing, versioning, and serving embeddings (vector representations of text, images, or other data) alongside traditional features. Integrates with vector databases to enable semantic search and similarity-based feature retrieval, treating embeddings as first-class feature types with the same versioning and lineage tracking as scalar features.","intents":["Store and version embeddings generated from text, images, or other unstructured data","Serve embeddings to models alongside traditional features","Enable semantic search and similarity-based recommendations using embeddings","Track which embedding model and version was used for reproducibility"],"best_for":["ML teams building recommendation systems or semantic search applications","Organizations using large language models to generate embeddings","Teams combining embeddings with traditional features in hybrid models"],"limitations":["Specific vector database integrations not documented; unclear which systems are supported","Embedding generation logic must be provided externally; no built-in embedding model support","Vector search query capabilities not specified; may not support advanced similarity metrics","Embedding versioning strategy not detailed; unclear how to handle model updates"],"requires":["Embedding generation pipeline (external model or service)","Vector database for similarity search (if using semantic search features)","Feature definitions specifying embedding dimensions and metadata"],"input_types":["Embeddings (fixed-size float vectors)","Embedding metadata (model name, version, generation timestamp)","Similarity search queries"],"output_types":["Embedding vectors","Similarity search results with scores","Embedding metadata and lineage"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_7","uri":"capability://tool.use.integration.real.time.feature.serving.with.low.latency.inference.caching","name":"real-time feature serving with low-latency inference caching","description":"Serves features to production models with sub-second latency by caching frequently-accessed features in Redis and routing requests to appropriate backends based on feature type (batch features from data warehouse, real-time features from cache). Supports both synchronous feature requests (single entity) and batch requests (multiple entities), with configurable cache TTLs and refresh policies.","intents":["Serve features to production models with latency requirements under 100ms","Cache hot features in Redis to avoid repeated data warehouse queries","Support both online (single-entity) and batch (multi-entity) feature serving","Handle traffic spikes without overwhelming backend storage systems"],"best_for":["ML teams deploying real-time models (fraud detection, recommendation, personalization)","Applications with strict latency requirements (sub-100ms feature serving)","High-traffic services where caching significantly reduces backend load"],"limitations":["Real-time feature serving (streaming updates) limited to Enterprise tier; open-source supports batch serving only","Cache invalidation strategy not documented; unclear how stale features are handled","No built-in feature request batching optimization; batch requests may not be optimized for throughput","Latency SLAs not specified; actual performance depends on Redis and backend system performance","Cache size limits not documented; unclear how to handle features exceeding available cache"],"requires":["Redis instance for caching (native integration)","Feature definitions specifying cache TTL and refresh policy","Network connectivity to Redis and backend storage systems"],"input_types":["Feature requests (entity IDs, feature names, timestamps)","Batch feature requests (multiple entity IDs)"],"output_types":["Feature vectors (feature name-value pairs)","Metadata about feature source (cache hit, backend query)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_8","uri":"capability://safety.moderation.role.based.access.control.and.sso.integration.for.feature.governance","name":"role-based access control and sso integration for feature governance","description":"Implements fine-grained access control over features, datasets, and transformations using role-based permissions, with support for SSO/SAML authentication and Okta integration. Enables organizations to restrict which teams can access, modify, or serve specific features, supporting compliance requirements and preventing unauthorized feature usage.","intents":["Restrict feature access to authorized teams based on roles and permissions","Integrate with corporate identity providers (Okta, SAML) for centralized access management","Audit who accessed or modified features for compliance and security","Prevent unauthorized feature usage in production models"],"best_for":["Regulated industries requiring strict access control (finance, healthcare, insurance)","Large organizations with multiple teams and complex permission requirements","Organizations with existing SSO/SAML infrastructure"],"limitations":["RBAC and SSO/Okta integration limited to Enterprise tier; open-source has no access control","Granularity of role-based permissions not documented; unclear if supporting feature-level or dataset-level control","Audit logging limited to Enterprise tier; open-source cannot track access history","No built-in attribute-based access control (ABAC); limited to role-based approach"],"requires":["Enterprise tier subscription","SSO/SAML provider or Okta instance (for SSO integration)","Role definitions and permission mappings"],"input_types":["User identity (from SSO provider)","Feature or dataset identifiers","Requested action (read, write, delete)"],"output_types":["Access decision (allow/deny)","Audit logs (Enterprise tier only)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__cap_9","uri":"capability://data.processing.analysis.feature.analysis.and.statistical.profiling.with.drift.baselines","name":"feature analysis and statistical profiling with drift baselines","description":"Automatically computes and tracks statistical summaries of features (mean, variance, quantiles, cardinality, missing value rates) and compares against historical baselines to detect anomalies. Provides feature-level statistics and analysis tools for understanding feature distributions, identifying outliers, and investigating data quality issues without requiring external data profiling tools.","intents":["Understand feature distributions and statistical properties without manual analysis","Detect outliers and anomalies in feature values","Compare feature statistics across time periods to identify changes","Investigate data quality issues by examining feature-level statistics"],"best_for":["Data engineers validating feature quality before production deployment","ML teams investigating model performance issues caused by feature anomalies","Organizations with data quality requirements"],"limitations":["Statistical profiling algorithms not documented; unclear which metrics are computed","Baseline calculation method not specified; may not handle non-stationary distributions","No built-in visualization of feature distributions; requires external tools for analysis","Profiling latency not specified; may be slow for high-cardinality features","Limited to features defined in Featureform; external data sources not profiled"],"requires":["Feature data with sufficient history for baseline calculation","Underlying storage system supporting statistical queries"],"input_types":["Feature values (numeric or categorical)","Historical feature data"],"output_types":["Statistical summaries (mean, variance, quantiles, cardinality, missing rates)","Baseline comparisons","Anomaly flags"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"featureform__headline","uri":"capability://data.processing.analysis.virtual.feature.store.for.machine.learning","name":"virtual feature store for machine learning","description":"Featureform is a virtual feature store that enables ML teams to manage feature versioning and serving without data migration, integrating seamlessly with existing data infrastructures.","intents":["best virtual feature store","feature store for machine learning","how to manage ML features without migration","top feature serving solutions for data teams","feature versioning tools for ML"],"best_for":["ML teams needing feature management"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","Access to underlying compute infrastructure (Databricks, Snowflake, or custom provider)","Basic understanding of feature engineering concepts","At least one supported data infrastructure system (Databricks, Snowflake, DynamoDB, MongoDB, Oracle, SAP, SAS, or Redis)","Network connectivity between Featureform and all backend systems","Appropriate credentials and permissions for each backend system","Features defined in Featureform with metadata (tags, descriptions, owners)","Consistent tagging and naming conventions","Compute infrastructure (Databricks, Snowflake, or custom provider)","Feature definitions with transformation logic"],"failure_modes":["Feature definitions are stored in Featureform's proprietary format, creating moderate vendor lock-in","No built-in IDE support or syntax highlighting beyond standard Python editors","Declarative API requires learning Featureform-specific abstractions rather than using raw SQL/Spark","Performance depends entirely on underlying storage system latency and throughput","No built-in query optimization across heterogeneous backends","Custom provider integrations limited to Enterprise tier, restricting flexibility for open-source users","Requires maintaining and monitoring multiple storage systems independently","Search capabilities not detailed; unclear if supporting full-text search, tag-based search, or both","No built-in feature recommendation system; search is manual","Metadata schema not documented; unclear what fields are searchable","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"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":"2026-05-24T12:16:21.548Z","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=featureform","compare_url":"https://unfragile.ai/compare?artifact=featureform"}},"signature":"ed12xjZ08M6KkN2l7ozaWxWzgeKkN2LylsHdbtSipZX7sJjdkVG4LB+Uk/4E0cqgARhSQhZ/tnXHml+aGKYvDA==","signedAt":"2026-06-22T12:34:24.276Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/featureform","artifact":"https://unfragile.ai/featureform","verify":"https://unfragile.ai/api/v1/verify?slug=featureform","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"}}