{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"polyaxon","slug":"polyaxon","name":"Polyaxon","type":"platform","url":"https://polyaxon.com","page_url":"https://unfragile.ai/polyaxon","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"polyaxon__cap_0","uri":"capability://memory.knowledge.experiment.tracking.with.automatic.metric.capture","name":"experiment-tracking-with-automatic-metric-capture","description":"Automatically captures and persists hyperparameters, metrics, visualizations, artifacts, and resource utilization from ML training runs without explicit logging code. Implements a centralized metrics aggregation layer that hooks into popular deep learning frameworks, storing all run metadata with unique content-addressed hashes for reproducibility and deduplication. Provides full lineage tracking from source code version to trained model outputs.","intents":["I want to track all hyperparameters and metrics from my training runs without manually logging each one","I need to compare results across hundreds of experiments to find the best performing model","I want to understand which dataset version and code commit produced a specific model"],"best_for":["ML teams running iterative experiments across multiple frameworks","researchers comparing hundreds of model variants","organizations requiring full reproducibility and audit trails for model governance"],"limitations":["Framework support is claimed as 'all popular' but specific tested versions and compatibility matrix are unknown","Automatic capture requires framework integration — custom training loops may need manual instrumentation","Metric visualization limited to Tensorboard and custom dashboards; no built-in statistical analysis tools mentioned"],"requires":["Kubernetes cluster or on-premise infrastructure","Polyaxon CLI or SDK installed in training environment","Supported ML framework (specific versions unknown)"],"input_types":["training metrics (scalars, histograms, distributions)","hyperparameter configurations (JSON/YAML)","model artifacts and checkpoints","training logs and stdout"],"output_types":["structured experiment metadata with unique hash identifiers","metric timeseries data","artifact lineage graph","comparison matrices and visualizations"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_1","uri":"capability://planning.reasoning.hyperparameter.optimization.with.distributed.execution","name":"hyperparameter-optimization-with-distributed-execution","description":"Executes parallel and distributed hyperparameter search across a Kubernetes cluster using built-in optimization algorithms to find optimal model configurations. Implements consensus-based early stopping strategies that terminate unpromising runs before completion, reducing wasted compute. Supports concurrent execution with tiered limits (50-1000 depending on subscription tier) and per-queue quota splitting for multi-team resource allocation.","intents":["I want to run 100+ hyperparameter combinations in parallel to find the best model faster","I need to stop underperforming experiments early to save GPU hours","I want to distribute hyperparameter search across multiple teams with fair resource allocation"],"best_for":["teams with large GPU clusters optimizing expensive models","organizations running continuous hyperparameter tuning pipelines","multi-team environments needing fair resource scheduling"],"limitations":["Optimization algorithms not enumerated — specific supported strategies (Bayesian, grid, random, etc.) unknown","Early stopping consensus definition is opaque — no documentation on how success thresholds are determined","Concurrent run limits are subscription-dependent (50 base, up to 1000 with additional cost); no auto-scaling of limits based on cluster capacity","No mention of warm-start capabilities or transfer learning from previous optimization runs"],"requires":["Kubernetes cluster with GPU nodes (specific GPU types unknown)","Polyaxon Platform tier or above ($450+/month)","Experiment configuration in JSON/YAML format with hyperparameter search space defined"],"input_types":["hyperparameter search space definition (ranges, distributions)","training script or containerized job","optimization algorithm selection","early stopping criteria and thresholds"],"output_types":["ranked list of hyperparameter configurations by performance","convergence plots and optimization history","best model checkpoint and associated hyperparameters","resource utilization metrics per run"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_10","uri":"capability://safety.moderation.role.based.access.control.with.service.accounts","name":"role-based-access-control-with-service-accounts","description":"Implements fine-grained role-based access control (RBAC) for experiments, models, pipelines, and queues. Supports multiple user roles (developer, read-only, admin) with tiered pricing (developers $79/month, read-only $9/month). Provides service accounts for CI/CD and continuous training workflows, enabling automated model promotion and job submission without human interaction. Integrates with external authentication systems (LDAP, OAuth, SAML).","intents":["I want to give junior developers read-only access to experiments without write permissions","I need to create a service account for CI/CD to automatically promote models to production","I want to integrate Polyaxon with our company's LDAP directory for user management"],"best_for":["enterprises with formal access control requirements","teams automating model promotion through CI/CD pipelines","organizations with centralized identity management (LDAP, OAuth)"],"limitations":["Fine-grained permission model not documented — unclear which operations each role can perform","Service account capabilities not detailed — no documentation on token management, rotation, or revocation","External authentication integration mechanism unknown — no documentation on LDAP/OAuth/SAML setup","No mention of resource-level access control (e.g., team-specific queues)","Audit trail for access control decisions not mentioned"],"requires":["Polyaxon Teams tier or above ($1,200+/month) for RBAC","User authentication system (LDAP, OAuth, SAML) for external integration","Service account credentials for CI/CD automation"],"input_types":["user identity and role assignment","service account creation request","external authentication provider configuration"],"output_types":["access control decision (allow/deny)","service account token","user role and permission list","authentication audit logs"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_11","uri":"capability://automation.workflow.schedule.based.job.triggering.with.concurrency.control","name":"schedule-based-job-triggering-with-concurrency-control","description":"Schedules recurring jobs and experiments using cron expressions or interval-based triggers. Enforces per-schedule concurrency limits (5-50 depending on tier) to prevent overlapping executions. Integrates with continuous training pipelines for automated model retraining on new data. Supports manual triggers (start, stop, resume, restart, copy) for ad-hoc job execution.","intents":["I want to retrain my model every night on the latest data","I need to prevent multiple training jobs from running simultaneously on the same model","I want to manually trigger a training job with the same configuration as a previous run"],"best_for":["teams implementing continuous training pipelines","organizations with automated model retraining requirements","projects requiring scheduled batch processing"],"limitations":["Schedule concurrency limits are tiered by subscription (5-50) — no auto-scaling based on cluster capacity","Cron expression support mentioned but specific syntax and timezone handling unknown","No mention of conditional scheduling based on data availability or metric thresholds","Failure recovery mechanism for scheduled jobs not documented","No mention of schedule versioning or rollback capabilities"],"requires":["Polyaxon platform instance","Job or experiment definition","Cron expression or interval specification","Concurrency limit configuration"],"input_types":["job/experiment definition","schedule trigger (cron expression or interval)","concurrency limit","manual trigger action (start, stop, resume, restart, copy)"],"output_types":["scheduled job execution status","execution history and logs","next scheduled execution time","concurrency queue status"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_12","uri":"capability://automation.workflow.cloud.agnostic.deployment.with.kubernetes.native.execution","name":"cloud-agnostic-deployment-with-kubernetes-native-execution","description":"Deploys Polyaxon on any Kubernetes cluster across AWS, Azure, GCP, or on-premise infrastructure without vendor lock-in. Implements native Kubernetes execution using standard Kubernetes APIs (Pods, Services, ConfigMaps) rather than custom CRDs, enabling compatibility with existing Kubernetes tooling and operators. Supports hybrid deployments combining on-premise and cloud resources. Provides cloud-agnostic artifact storage abstraction supporting S3, GCS, Azure Blob, and on-premise backends.","intents":["I want to run Polyaxon on our existing Kubernetes cluster without vendor lock-in","I need to deploy Polyaxon across multiple cloud providers simultaneously","I want to keep all data on-premise while using cloud for burst capacity"],"best_for":["enterprises with multi-cloud strategies","organizations with strict data residency requirements","teams with existing Kubernetes infrastructure"],"limitations":["Kubernetes version requirements not documented","Artifact storage backend configuration details unknown","Network isolation and security group configuration not detailed","Multi-region deployment and failover mechanisms not documented","Kubernetes operator compatibility matrix unknown"],"requires":["Kubernetes 1.16+ (specific version unknown)","Helm 3+ for deployment","Artifact storage backend (S3, GCS, Azure Blob, or NFS)","Network connectivity between Kubernetes nodes and artifact storage"],"input_types":["Kubernetes cluster configuration","Helm values for Polyaxon deployment","Artifact storage credentials and endpoint"],"output_types":["Polyaxon control plane deployment","Kubernetes resources (Pods, Services, ConfigMaps)","artifact storage configuration","deployment status and health checks"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_13","uri":"capability://tool.use.integration.integration.hooks.and.external.system.connectivity","name":"integration-hooks-and-external-system-connectivity","description":"Provides webhook-based integration hooks enabling Polyaxon to trigger external systems on job completion, model promotion, or other events. Supports custom actions for integrating with external platforms (Slack, email, webhooks). Enables bidirectional integration through REST API for querying experiment status, submitting jobs, and retrieving artifacts. Service accounts support CI/CD integration for automated workflows.","intents":["I want to send a Slack notification when a model is promoted to production","I need to trigger a deployment pipeline when training completes","I want to query Polyaxon from my CI/CD system to check if a model is ready for deployment"],"best_for":["teams integrating Polyaxon into existing CI/CD and DevOps workflows","organizations with custom notification and alerting requirements","projects requiring bidirectional integration with external systems"],"limitations":["Webhook event types and payload schema not documented","REST API endpoints and authentication mechanism not detailed","Custom action implementation mechanism unknown","Rate limiting and retry policies not specified","No mention of webhook delivery guarantees or failure handling"],"requires":["Polyaxon platform instance","External system endpoint (webhook URL, API endpoint)","Service account credentials for API authentication"],"input_types":["webhook event trigger (job completion, model promotion, etc.)","REST API request (query experiment, submit job, retrieve artifact)","custom action definition"],"output_types":["webhook payload sent to external system","REST API response (experiment metadata, job status, artifact URL)","integration status and error logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_14","uri":"capability://code.generation.editing.interactive.workspace.with.notebook.support","name":"interactive-workspace-with-notebook-support","description":"Provides interactive development environments (Jupyter notebooks, JupyterLab) for exploratory analysis and model development. Integrates with experiment tracking to automatically log metrics and artifacts from notebook cells. Allocates compute resources (CPU, GPU, memory) to notebook sessions with configurable limits. Supports persistent storage for notebooks and data across sessions.","intents":["I want to develop and test my model in a Jupyter notebook with GPU support","I need to automatically log metrics from my notebook exploration to the experiment tracker","I want to save my notebook and data between sessions without losing work"],"best_for":["data scientists doing exploratory analysis and prototyping","teams developing models interactively before scaling to production","researchers requiring GPU-accelerated notebooks"],"limitations":["Notebook compute allocation mechanism unknown — no documentation on resource requests/limits","Automatic metric logging from notebooks not detailed — unclear which cell outputs are captured","Persistent storage backend and capacity limits not documented","Multi-user notebook sharing and collaboration not mentioned","Integration with experiment tracking from notebooks not detailed"],"requires":["Polyaxon platform instance","GPU nodes for accelerated notebooks (optional)","Persistent storage backend (S3, GCS, Azure Blob, or NFS)"],"input_types":["notebook code and markdown","resource allocation (CPU, GPU, memory)","notebook configuration (Python version, packages)"],"output_types":["interactive notebook execution results","automatically logged metrics and artifacts","notebook checkpoints and version history","persistent storage for notebooks and data"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_2","uri":"capability://automation.workflow.model.registry.with.promotion.workflow","name":"model-registry-with-promotion-workflow","description":"Maintains a versioned model registry that locks experiments and enables promotion of trained models through deployment stages (staging, production, etc.). Each model version is immutable and linked to its source experiment, training data version, and code commit. Provides role-based access control for promotion decisions and audit trails of all state transitions.","intents":["I want to lock a trained model and promote it to production with approval workflows","I need to track which dataset and code version produced each deployed model","I want to prevent accidental overwriting of production models"],"best_for":["organizations with formal model governance and approval processes","teams deploying models to production with compliance requirements","enterprises needing audit trails for regulatory reporting"],"limitations":["Production deployment mechanism is unknown — registry exists but serving/inference infrastructure not detailed","Promotion workflow customization not documented — approval stages and role definitions unclear","No mention of model rollback capabilities or A/B testing support","Integration with model serving platforms (KServe, Seldon, etc.) not specified"],"requires":["Polyaxon platform instance with model registry enabled","Completed experiment with trained model artifact","Role-based access control configured (Teams tier or above)"],"input_types":["trained model artifact from experiment","promotion stage definition","approval decision from authorized user"],"output_types":["immutable model version with unique identifier","promotion history and audit trail","metadata linking to source experiment, dataset, and code version","deployment configuration for serving"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_3","uri":"capability://automation.workflow.pipeline.orchestration.with.dag.execution","name":"pipeline-orchestration-with-dag-execution","description":"Orchestrates multi-step ML workflows as directed acyclic graphs (DAGs) combining experiments, jobs, and services with typed inputs/outputs. Executes pipeline steps sequentially or in parallel based on dependency graph, with built-in retry logic, timeout enforcement, and TTL-based cleanup. Supports component reuse through a Component Hub that extracts parameterized modules with schema-based interfaces.","intents":["I want to chain multiple training and evaluation steps into a single reproducible workflow","I need to run data preprocessing, training, and evaluation in sequence with automatic error recovery","I want to reuse common pipeline components across multiple projects"],"best_for":["teams building complex multi-stage ML workflows","organizations with standardized ML processes requiring component reuse","projects needing automated retry and timeout handling"],"limitations":["Component Hub details are unknown — no documentation on discoverability, sharing mechanisms, or community contributions","DAG visualization and debugging tools not mentioned","No mention of conditional branching or dynamic pipeline generation based on runtime values","Integration with external orchestrators (Airflow, Prefect) not documented"],"requires":["Polyaxon platform instance","Pipeline definition in YAML or Python format","Kubernetes cluster for execution","Component definitions with typed inputs/outputs"],"input_types":["pipeline DAG definition (YAML/Python)","component specifications with input/output schemas","runtime parameters and configuration","artifact references from previous steps"],"output_types":["pipeline execution status and logs","step-by-step execution timeline","artifact outputs from each pipeline stage","resource utilization per step"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_4","uri":"capability://automation.workflow.distributed.training.with.operator.support","name":"distributed-training-with-operator-support","description":"Executes distributed training jobs across Kubernetes clusters using native operators for Kubeflow, Ray, Dask, and Spark. Abstracts underlying distributed training framework complexity through a unified job submission interface, automatically handling distributed configuration, communication setup, and resource allocation across worker nodes. Supports horizontal scaling by adding nodes and GPUs without job reconfiguration.","intents":["I want to scale my training job from single-GPU to multi-node without rewriting code","I need to run distributed training with Ray/Dask/Spark without managing cluster configuration","I want to add more GPUs or nodes to an existing training job dynamically"],"best_for":["teams training large models requiring multi-node parallelism","organizations with heterogeneous training frameworks (Ray, Dask, Spark)","projects needing dynamic resource scaling during training"],"limitations":["Supported operators and their compatibility matrix are unknown — specific versions and tested configurations not documented","Custom operator development mechanism is unknown","No mention of distributed training debugging tools or profiling","Spot instance integration mentioned but not detailed — no documentation on fault tolerance or preemption handling","GPU type specifications unknown — no guidance on which operators support which hardware"],"requires":["Kubernetes cluster with multiple nodes","GPU nodes for accelerated training (specific types unknown)","Supported distributed training framework (Ray, Dask, Spark, or Kubeflow)","Job definition specifying number of workers and resource requirements"],"input_types":["training script compatible with distributed framework","worker count and resource specifications","operator selection (Ray/Dask/Spark/Kubeflow)","communication backend configuration"],"output_types":["distributed job status and worker logs","training metrics aggregated across workers","resource utilization per worker node","trained model checkpoint"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_5","uri":"capability://memory.knowledge.artifact.versioning.and.lineage.tracking","name":"artifact-versioning-and-lineage-tracking","description":"Versions datasets and model artifacts with immutable content-addressed identifiers, tracking provenance across data transformations, training runs, and model deployments. Implements a lineage graph connecting artifacts to their source experiments, code versions, and downstream consumers. Enables querying artifacts by metadata, searching for specific versions, and understanding data flow through the ML pipeline.","intents":["I want to know which dataset version was used to train a specific model","I need to find all models trained on a particular dataset version","I want to trace how data flows through preprocessing, training, and evaluation stages"],"best_for":["organizations with complex data pipelines requiring full provenance tracking","teams debugging model performance issues by tracing to source data","enterprises needing data governance and compliance audit trails"],"limitations":["Lineage graph query language and capabilities are unknown","No mention of lineage visualization tools or graph exploration interfaces","Artifact storage backend and scalability limits not documented","Export mechanisms for lineage data unknown — no documentation on data portability","No mention of data quality metrics or anomaly detection in lineage"],"requires":["Polyaxon platform instance","Artifact storage backend (S3, GCS, Azure Blob, or on-premise)","Experiments or jobs producing versioned artifacts"],"input_types":["artifact files (datasets, models, checkpoints)","metadata tags and descriptions","experiment or job context"],"output_types":["immutable artifact version with content hash","lineage graph showing artifact dependencies","metadata and provenance information","artifact search results filtered by version, date, or experiment"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_6","uri":"capability://search.retrieval.experiment.comparison.and.visualization","name":"experiment-comparison-and-visualization","description":"Provides multi-dimensional comparison of experiment results across hyperparameters, metrics, training data versions, and source code commits. Implements search and filtering by name, description, regex patterns, specific fields, and metric ranges. Supports custom visualization dashboards alongside built-in Tensorboard integration, enabling side-by-side analysis of hundreds of experiments to identify patterns and optimal configurations.","intents":["I want to compare 50 experiments to see which hyperparameters had the biggest impact on accuracy","I need to visualize how model performance changed across different dataset versions","I want to search for all experiments with validation loss below a threshold"],"best_for":["data scientists iterating on model architectures and hyperparameters","teams analyzing experiment results to understand feature importance","researchers publishing papers requiring detailed experiment comparisons"],"limitations":["Custom visualization capabilities not detailed — no documentation on supported chart types or dashboard customization","Search query language and performance characteristics unknown","No mention of statistical significance testing or confidence intervals","Tensorboard integration is mentioned but specific version support unknown","No built-in statistical analysis tools (correlation, regression, clustering) mentioned"],"requires":["Polyaxon platform instance with completed experiments","Metrics and hyperparameters captured during training","Web browser for dashboard access"],"input_types":["experiment metadata (hyperparameters, metrics, code version, dataset version)","search queries (name, description, regex, field-based filters)","visualization preferences (chart type, axes, grouping)"],"output_types":["comparison matrices and tables","multi-dimensional visualizations (scatter plots, parallel coordinates, heatmaps)","Tensorboard integration for detailed metric inspection","custom dashboard views"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_7","uri":"capability://automation.workflow.resource.monitoring.and.quota.enforcement","name":"resource-monitoring-and-quota-enforcement","description":"Monitors CPU, memory, GPU, and storage utilization across all running jobs and experiments. Enforces global concurrency limits and per-queue/workflow quotas to prevent resource exhaustion, with automatic queue-based scheduling when limits are reached. Provides per-job resource metrics and historical utilization trends for capacity planning. Supports spot instance integration for cost optimization.","intents":["I want to see how much GPU memory each training job is using","I need to prevent one team from monopolizing all GPUs while other teams wait","I want to use spot instances to reduce training costs without losing fault tolerance"],"best_for":["organizations with shared GPU clusters and multiple teams","teams optimizing cloud costs through spot instance usage","operations teams managing resource capacity and planning upgrades"],"limitations":["Spot instance integration mentioned but not detailed — no documentation on fault tolerance, preemption handling, or cost savings quantification","Queue scheduling algorithm not documented — no specification of fairness guarantees or priority mechanisms","Resource metrics granularity unknown — unclear if per-container or per-node level","No mention of resource prediction or auto-scaling recommendations","Quota enforcement mechanism opaque — no documentation on how queued jobs are prioritized"],"requires":["Kubernetes cluster with resource requests/limits configured","Polyaxon agents deployed on worker nodes","Queue definitions with resource quotas"],"input_types":["job resource requests (CPU, memory, GPU count)","queue quota specifications","global concurrency limits"],"output_types":["per-job resource utilization metrics (CPU, memory, GPU, storage)","queue status and waiting job counts","historical resource usage trends","cost estimates for spot vs. on-demand instances"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_8","uri":"capability://search.retrieval.log.streaming.and.search","name":"log-streaming-and-search","description":"Streams, filters, and searches logs from all training jobs, experiments, and pipeline steps in real-time. Implements full-text search with regex support and field-based filtering (timestamp, log level, component). Provides log aggregation across distributed training workers without requiring external logging infrastructure. Supports structured logging with JSON parsing for metric extraction from application logs.","intents":["I want to see training logs in real-time as my job runs","I need to search for errors across 100 training jobs to debug a common issue","I want to extract metrics from application logs and correlate them with tracked metrics"],"best_for":["teams debugging training failures across distributed jobs","developers monitoring long-running training jobs","operations teams investigating production model issues"],"limitations":["Log retention policy not documented — unclear how long logs are stored","Search performance characteristics unknown — no documentation on query latency for large log volumes","Structured logging support mentioned but JSON parsing capabilities not detailed","No mention of log sampling or compression for cost optimization","Integration with external logging systems (ELK, Splunk) not documented"],"requires":["Polyaxon platform instance","Jobs/experiments producing logs","Web browser or CLI for log access"],"input_types":["application logs from training jobs","search queries (full-text, regex, field-based filters)","log level filters (DEBUG, INFO, WARNING, ERROR)"],"output_types":["real-time log stream","filtered log results matching search criteria","structured log data (JSON parsed)","log statistics (error counts, warning trends)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__cap_9","uri":"capability://safety.moderation.activity.audit.trail.and.compliance.logging","name":"activity-audit-trail-and-compliance-logging","description":"Records all user actions (experiment creation, model promotion, configuration changes) with timestamps, user identity, and change details. Maintains immutable audit logs with configurable retention (3 months standard, custom for Enterprise). Enables compliance reporting and forensic investigation of model governance decisions. Integrates with role-based access control to enforce approval workflows.","intents":["I need to prove which user promoted a model to production and when","I want to audit all changes to experiment configurations for compliance reporting","I need to investigate who modified a model's metadata and what changed"],"best_for":["enterprises with regulatory compliance requirements (HIPAA, SOC2, GDPR)","organizations with formal model governance and approval workflows","teams requiring forensic investigation capabilities"],"limitations":["Audit log retention is tiered by subscription (3 months standard, custom for Enterprise) — no mention of archival or long-term storage options","Compliance certifications not documented — no mention of SOC2, HIPAA, GDPR compliance","Audit log query capabilities unknown — no documentation on filtering or reporting tools","Integration with external compliance systems (Splunk, Datadog) not mentioned","No mention of immutability guarantees or tamper detection"],"requires":["Polyaxon Teams tier or above ($1,200+/month)","Role-based access control configured","User authentication system (LDAP, OAuth, SAML)"],"input_types":["user actions (create, update, delete, promote operations)","configuration changes","approval decisions"],"output_types":["immutable audit log entries with timestamp and user identity","change history showing before/after values","compliance reports and audit summaries","access control decision logs"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polyaxon__headline","uri":"capability://data.processing.analysis.machine.learning.lifecycle.management.platform","name":"machine learning lifecycle management platform","description":"Polyaxon is a comprehensive machine learning platform designed for managing the full lifecycle of ML experiments, including hyperparameter optimization, distributed training, and model deployment on Kubernetes, tailored for enterprise governance.","intents":["best machine learning platform","machine learning lifecycle management for enterprises","top tools for ML experiment management","Kubernetes model deployment solutions","hyperparameter optimization tools for ML"],"best_for":["enterprises managing ML workflows"],"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":["Kubernetes cluster or on-premise infrastructure","Polyaxon CLI or SDK installed in training environment","Supported ML framework (specific versions unknown)","Kubernetes cluster with GPU nodes (specific GPU types unknown)","Polyaxon Platform tier or above ($450+/month)","Experiment configuration in JSON/YAML format with hyperparameter search space defined","Polyaxon Teams tier or above ($1,200+/month) for RBAC","User authentication system (LDAP, OAuth, SAML) for external integration","Service account credentials for CI/CD automation","Polyaxon platform instance"],"failure_modes":["Framework support is claimed as 'all popular' but specific tested versions and compatibility matrix are unknown","Automatic capture requires framework integration — custom training loops may need manual instrumentation","Metric visualization limited to Tensorboard and custom dashboards; no built-in statistical analysis tools mentioned","Optimization algorithms not enumerated — specific supported strategies (Bayesian, grid, random, etc.) unknown","Early stopping consensus definition is opaque — no documentation on how success thresholds are determined","Concurrent run limits are subscription-dependent (50 base, up to 1000 with additional cost); no auto-scaling of limits based on cluster capacity","No mention of warm-start capabilities or transfer learning from previous optimization runs","Fine-grained permission model not documented — unclear which operations each role can perform","Service account capabilities not detailed — no documentation on token management, rotation, or revocation","External authentication integration mechanism unknown — no documentation on LDAP/OAuth/SAML setup","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":"2026-05-24T12:16:25.060Z","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=polyaxon","compare_url":"https://unfragile.ai/compare?artifact=polyaxon"}},"signature":"u8j5byk0cIQ+FnDCStLhxvW316x2mmFYKRUTq2p94EfxiVywB8ZAoGRkFwwujS+YcriRPBNxgspsU425H6S/BA==","signedAt":"2026-06-22T11:03:01.221Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/polyaxon","artifact":"https://unfragile.ai/polyaxon","verify":"https://unfragile.ai/api/v1/verify?slug=polyaxon","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"}}