{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"aws-sagemaker","slug":"aws-sagemaker","name":"AWS SageMaker","type":"platform","url":"https://aws.amazon.com/sagemaker","page_url":"https://unfragile.ai/aws-sagemaker","categories":["model-training"],"tags":[],"pricing":{"model":"usage-based","free":true,"starting_price":"$0.05/hr"},"status":"active","verified":false},"capabilities":[{"id":"aws-sagemaker__cap_0","uri":"capability://code.generation.editing.managed.jupyter.notebook.environments.with.built.in.ai.assistant","name":"managed jupyter notebook environments with built-in ai assistant","description":"Provides fully managed Jupyter-based notebook instances hosted on AWS infrastructure with integrated Amazon Q Developer assistant for code generation, data exploration, and ML pipeline creation. Notebooks are pre-configured with common ML libraries and direct S3/Redshift access, eliminating local environment setup. The built-in AI agent generates SQL queries, discovers data sources, and scaffolds training code through natural language prompts.","intents":["I want to explore data and build ML models without managing compute infrastructure","I need an AI assistant that understands my AWS data sources and can generate boilerplate training code","I want to prototype ML workflows quickly without setting up Jupyter locally"],"best_for":["Data scientists prototyping models on AWS data lakes","Teams building ML workflows with S3/Redshift backends","Organizations wanting managed notebook infrastructure without DevOps overhead"],"limitations":["Notebooks are AWS-hosted only; no option for local execution or hybrid deployment","Amazon Q assistant capabilities limited to AWS-native data sources and SageMaker APIs","Notebook state tied to AWS account; migration to other platforms requires manual export","Serverless notebook option has unknown cold-start latency and scaling characteristics"],"requires":["AWS account with SageMaker service access","IAM permissions for S3 and Redshift access","Web browser for notebook UI access"],"input_types":["Python code","SQL queries","Natural language prompts to Amazon Q"],"output_types":["Jupyter notebooks","Generated Python training code","Generated SQL queries","Data visualizations"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_1","uri":"capability://automation.workflow.distributed.model.training.with.automatic.hyperparameter.optimization","name":"distributed model training with automatic hyperparameter optimization","description":"Orchestrates distributed training jobs across multiple compute instances using a managed training job abstraction that handles data distribution, checkpoint management, and fault recovery. Automatic Model Tuning (AMT) layer runs Bayesian optimization over hyperparameter search spaces, launching parallel training jobs and selecting best-performing configurations based on user-defined metrics. Training jobs pull data from S3, log metrics to CloudWatch, and persist models back to S3 automatically.","intents":["I need to train large models across multiple GPUs/TPUs without managing distributed training infrastructure","I want to automatically search hyperparameter space and find optimal configurations without manual experimentation","I need fault-tolerant training with automatic checkpointing and recovery"],"best_for":["ML teams training large models (>1GB) requiring multi-instance distribution","Organizations with limited ML infrastructure expertise wanting managed training","Researchers exploring hyperparameter sensitivity across large search spaces"],"limitations":["Specific GPU/CPU instance types and availability not documented; requires AWS pricing calculator to determine costs","Automatic Model Tuning adds latency for Bayesian optimization; no documented SLA for tuning job completion time","Training job logs and metrics stored in CloudWatch; no built-in experiment tracking UI (requires third-party tools or manual CloudWatch queries)","No documented support for custom distributed training frameworks beyond PyTorch/TensorFlow; custom training code must conform to SageMaker training container contract","Hyperparameter tuning limited to numeric and categorical parameters; no support for conditional hyperparameter dependencies"],"requires":["AWS account with SageMaker training permissions","Training data in S3 or accessible via Redshift","Training script compatible with SageMaker training container (Python, R, or custom Docker image)","IAM role with S3 read/write and CloudWatch logging permissions"],"input_types":["Training scripts (Python/R/custom Docker)","Hyperparameter search space definitions (JSON)","Training data (S3 objects, Redshift tables)"],"output_types":["Trained model artifacts (S3 objects)","Training metrics (CloudWatch logs)","Hyperparameter tuning results (best configuration JSON)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_10","uri":"capability://automation.workflow.multi.model.endpoints.with.shared.infrastructure","name":"multi-model endpoints with shared infrastructure","description":"Deploys multiple trained models to a single inference endpoint, enabling efficient resource utilization and simplified model management. Models are loaded into shared container instances and invoked by specifying the target model name in the request. Supports independent scaling per model and A/B testing across models. Reduces infrastructure costs by consolidating multiple low-traffic models onto shared instances.","intents":["I want to deploy multiple models without provisioning separate endpoints for each","I need to test multiple model versions simultaneously with traffic splitting","I want to reduce infrastructure costs by consolidating low-traffic models"],"best_for":["Organizations with many low-traffic models (e.g., per-customer or per-segment models)","Teams needing efficient resource utilization for model portfolios","ML systems requiring A/B testing across multiple model variants"],"limitations":["Model loading and invocation latency not documented; unclear if model switching adds latency","Memory and compute constraints for shared instances not specified; no guidance on how many models can coexist","Independent scaling per model not detailed; unclear how traffic is allocated when models have different load patterns","Model isolation and failure handling not documented; unclear if one model's failure affects others","Supported model types and frameworks not specified; unclear if all SageMaker-compatible models work in multi-model endpoints"],"requires":["Multiple trained model artifacts in S3","Multi-model endpoint configuration (model names, versions, traffic weights)","SageMaker inference container supporting multi-model loading"],"input_types":["Model artifacts (S3 objects)","Inference requests with target model name","Traffic splitting configuration"],"output_types":["Inference predictions from selected model","Per-model metrics and latency"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_11","uri":"capability://safety.moderation.model.monitoring.and.drift.detection","name":"model monitoring and drift detection","description":"Continuously monitors deployed model endpoints for data drift (input distribution changes), prediction drift (output distribution changes), and feature attribution drift. Compares production data against training data baselines and alerts when drift exceeds configured thresholds. Integrates with CloudWatch for alerting and provides dashboards for drift visualization. Supports custom metrics and drift detection algorithms.","intents":["I want to detect when my model's input data distribution changes and alert on potential performance degradation","I need to monitor prediction drift to identify when model retraining is necessary","I want to understand which features are driving model predictions and detect when their importance shifts"],"best_for":["Teams operating models in production requiring continuous quality monitoring","Organizations with regulatory requirements for model audit trails and performance tracking","ML systems with non-stationary data requiring automated retraining triggers"],"limitations":["Drift detection algorithms and sensitivity tuning not documented; unclear how thresholds are set or optimized","Baseline establishment and update strategy not detailed; unclear how training data baselines are maintained","Custom metric implementation not specified; no clarity on how domain-specific drift indicators are defined","Alert latency and detection delay not documented; no SLA for drift notification timing","Integration with automated retraining pipelines not mentioned; unclear how drift detection triggers model retraining","Feature attribution methods not specified; unclear which algorithms are used (SHAP, LIME, etc.)"],"requires":["Deployed SageMaker endpoint","Training data baseline for comparison","Drift detection configuration (metrics, thresholds, check frequency)","CloudWatch integration for alerting"],"input_types":["Production inference data (from endpoint logs)","Training data baseline","Drift detection rules (thresholds, algorithms)"],"output_types":["Drift detection alerts","Drift dashboards and visualizations","Feature importance changes","CloudWatch metrics"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_12","uri":"capability://automation.workflow.asynchronous.inference.with.s3.based.request.response.handling","name":"asynchronous inference with s3-based request/response handling","description":"Enables asynchronous model inference for long-running predictions by accepting requests from S3 input locations and writing predictions to S3 output locations. Clients submit inference requests with S3 URIs and receive output location URIs without waiting for completion. Useful for batch-like inference with unpredictable latency or large payloads. Automatically scales inference capacity based on queue depth.","intents":["I want to run inference on large payloads without blocking the client","I need to decouple inference request submission from result retrieval","I want to handle variable inference latency without maintaining persistent endpoints"],"best_for":["Applications with long-running inference (>30 seconds) or large payloads","Systems requiring decoupled request/response patterns","Workloads with variable inference latency and bursty traffic patterns"],"limitations":["Queue depth and auto-scaling behavior not documented; no SLA for request processing latency","Request timeout and retry policies not specified; unclear how failed requests are handled","S3 output location format and result retrieval mechanism not detailed; unclear how clients poll for results","Payload size limits not documented; no guidance on maximum request/response sizes","Integration with result notification (SNS, SQS) not mentioned; clients may need to poll S3 for results","Cost model for asynchronous inference not provided; unclear how pricing differs from real-time endpoints"],"requires":["Deployed SageMaker endpoint configured for asynchronous inference","S3 buckets for input and output data","IAM permissions for S3 read/write","Client code to submit requests and retrieve results from S3"],"input_types":["Inference requests (S3 URIs pointing to input data)","Request configuration (output location, timeout)"],"output_types":["Output location URIs (S3 paths to predictions)","Inference results (S3 objects)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_13","uri":"capability://automation.workflow.hyperpod.managed.infrastructure.for.large.scale.model.development","name":"hyperpod: managed infrastructure for large-scale model development","description":"Provides managed compute clusters optimized for large-scale model training and development, handling infrastructure provisioning, networking, and fault recovery. Clusters support distributed training frameworks (PyTorch, TensorFlow) and enable researchers to focus on model development without managing infrastructure. Includes automatic node provisioning, inter-node networking optimization, and checkpoint management.","intents":["I want to train large foundation models without managing distributed infrastructure","I need reliable, fault-tolerant compute clusters for long-running training jobs","I want to scale training across hundreds of GPUs without manual infrastructure configuration"],"best_for":["Teams training large foundation models (>10B parameters)","Organizations requiring managed infrastructure for distributed training","Researchers needing reliable, fault-tolerant compute without DevOps overhead"],"limitations":["HyperPod architecture, cluster configuration options, and scaling limits not documented in provided materials","Supported instance types and GPU configurations not specified; no guidance on cluster sizing","Fault recovery mechanisms and checkpoint management not detailed; unclear how node failures are handled","Networking optimization and inter-node latency not documented; no performance benchmarks provided","Cost model and pricing structure not provided; requires AWS pricing calculator","Integration with training frameworks and custom training code not detailed; unclear what modifications are needed"],"requires":["AWS account with SageMaker HyperPod access","Distributed training code compatible with PyTorch or TensorFlow","Training data accessible from cluster (S3, Redshift, or local storage)","IAM permissions for cluster provisioning and resource access"],"input_types":["Cluster configuration (instance types, node count, networking)","Training code (PyTorch/TensorFlow)","Training data"],"output_types":["Trained model artifacts","Training logs and metrics","Cluster status and utilization metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_2","uri":"capability://automation.workflow.one.click.model.deployment.to.real.time.inference.endpoints","name":"one-click model deployment to real-time inference endpoints","description":"Converts trained model artifacts into production-ready inference endpoints through a declarative deployment abstraction that handles container orchestration, auto-scaling configuration, and traffic routing. Users specify model artifact location, instance type, and initial capacity; SageMaker provisions infrastructure, exposes REST/gRPC endpoints, and manages rolling updates. Endpoints automatically scale based on request volume (auto-scaling specifics undocumented) and support A/B testing via traffic splitting.","intents":["I want to deploy a trained model to production without writing deployment code or managing Kubernetes","I need a REST API endpoint for real-time model inference with automatic scaling","I want to test new model versions against production traffic using A/B testing"],"best_for":["Teams deploying models to production without DevOps expertise","Organizations requiring managed inference infrastructure with AWS billing integration","ML teams needing rapid model iteration with A/B testing capabilities"],"limitations":["Auto-scaling behavior and scaling metrics not documented; no visibility into scaling policies or latency SLAs","Endpoint pricing structure not provided; requires AWS pricing calculator for cost estimation","No documented support for custom inference containers beyond SageMaker-provided images; custom containers must conform to undocumented contract","A/B testing limited to traffic splitting; no built-in statistical significance testing or automated winner selection","Cold-start latency for new endpoint instances unknown; no documented warm-up or pre-scaling mechanisms","Endpoint state tied to AWS account; no multi-region failover or disaster recovery built-in"],"requires":["Trained model artifact in S3","AWS account with SageMaker endpoint creation permissions","IAM role with S3 read and CloudWatch logging permissions","Model compatible with SageMaker inference container (PyTorch, TensorFlow, XGBoost, etc.) or custom Docker image"],"input_types":["Model artifacts (S3 objects)","Endpoint configuration (instance type, initial capacity, traffic splitting rules)"],"output_types":["REST API endpoint URL","Inference predictions (JSON)","Endpoint metrics (CloudWatch)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_3","uri":"capability://automation.workflow.batch.transform.jobs.for.asynchronous.large.scale.inference","name":"batch transform jobs for asynchronous large-scale inference","description":"Processes large datasets through trained models without maintaining persistent endpoints by submitting batch inference jobs that read input data from S3, invoke the model on mini-batches, and write predictions back to S3. Jobs automatically partition data across multiple instances for parallel processing and handle fault recovery. Useful for offline scoring, feature generation, or periodic model evaluation on large datasets.","intents":["I need to score millions of records through a model without paying for persistent endpoint infrastructure","I want to generate predictions for a large dataset and store results in S3 for downstream processing","I need to run periodic batch inference jobs on a schedule without managing job orchestration"],"best_for":["Teams performing offline model scoring on large datasets (>1GB)","Organizations with periodic batch inference needs (daily/weekly scoring)","ML pipelines requiring cost-effective inference without real-time latency requirements"],"limitations":["Batch transform job latency and throughput not documented; no SLA for job completion time","Instance type selection and parallelization strategy not exposed to users; automatic partitioning behavior undocumented","No built-in result validation or quality checks; users must implement downstream validation","Job output format limited to S3 objects; no direct integration with data warehouses or feature stores for result storage","Fault recovery behavior undocumented; unclear how partial failures are handled or retried"],"requires":["Trained model artifact in S3","Input data in S3 (CSV, JSON, Parquet, or custom format)","AWS account with SageMaker batch transform permissions","IAM role with S3 read/write permissions"],"input_types":["Model artifacts (S3 objects)","Input data (S3 CSV/JSON/Parquet files)","Batch job configuration (input/output paths, instance type)"],"output_types":["Predictions (S3 JSON/CSV files)","Job status and metrics (CloudWatch)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_4","uri":"capability://automation.workflow.mlops.pipeline.orchestration.with.dag.based.workflow.definition","name":"mlops pipeline orchestration with dag-based workflow definition","description":"Defines machine learning workflows as directed acyclic graphs (DAGs) where nodes represent training jobs, batch transforms, model evaluations, or conditional logic, and edges define data dependencies. Pipelines are defined declaratively (YAML or Python SDK), stored in version control, and executed on a managed orchestration engine that handles job scheduling, data passing between steps, and conditional branching. Integrates with SageMaker training, tuning, and deployment steps natively.","intents":["I want to automate the entire ML workflow from data preparation through model deployment without writing orchestration code","I need to define reusable ML pipelines that can be triggered on schedule or by external events","I want version-controlled, reproducible ML workflows with built-in lineage tracking"],"best_for":["ML teams implementing MLOps practices with automated retraining pipelines","Organizations requiring reproducible, version-controlled ML workflows","Teams needing conditional logic in ML pipelines (e.g., deploy only if accuracy improves)"],"limitations":["Pipeline definition language and syntax not documented in provided materials; requires AWS documentation or SDK exploration","Conditional branching logic and parameter passing between steps not detailed; unclear how complex dependencies are expressed","Pipeline execution monitoring and debugging tools not documented; no visibility into step-level logs or failure diagnostics","No built-in integration with external orchestration tools (Airflow, Prefect); pipelines are SageMaker-specific","Lineage tracking and artifact versioning capabilities not documented; unclear how pipeline history is maintained","Trigger mechanisms (schedule, event-based) not specified; no documented support for webhook-based triggers"],"requires":["AWS account with SageMaker pipeline creation permissions","Training/inference components compatible with SageMaker (training jobs, batch transforms, etc.)","IAM role with permissions for all pipeline step components","Pipeline definition in YAML or Python SDK format"],"input_types":["Pipeline definition (YAML or Python)","Training/inference component specifications","Parameter overrides (JSON)"],"output_types":["Pipeline execution status","Step-level artifacts (models, metrics, predictions)","Execution logs (CloudWatch)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_5","uri":"capability://code.generation.editing.amazon.q.developer.natural.language.ml.code.generation.and.data.discovery","name":"amazon q developer: natural language ml code generation and data discovery","description":"Generative AI assistant integrated into SageMaker notebooks and development environments that generates ML training code, SQL queries, and data pipeline definitions from natural language prompts. The assistant has built-in knowledge of AWS data sources (S3 buckets, Redshift schemas, DataZone catalogs) and SageMaker APIs, enabling context-aware code generation without manual schema specification. Supports data discovery queries like 'find tables with customer demographics' and generates corresponding SQL or data loading code.","intents":["I want to generate training code for a model without writing boilerplate PyTorch/TensorFlow code","I need to discover relevant data sources in my AWS data lake using natural language queries","I want to generate SQL queries and data pipelines from English descriptions of data transformations"],"best_for":["Data scientists with limited Python/SQL expertise wanting to accelerate code generation","Teams exploring large data lakes and needing natural language data discovery","Organizations onboarding new ML practitioners who need scaffolding for common patterns"],"limitations":["Amazon Q capabilities limited to AWS-native data sources; no support for external databases or data warehouses","Generated code quality and correctness not documented; no metrics on code accuracy or need for manual review","Data discovery limited to DataZone-registered catalogs; no support for unregistered S3 buckets or ad-hoc data sources","No documented support for complex ML patterns (distributed training, custom loss functions, advanced architectures)","Hallucination risk for generated code not addressed; no built-in validation or testing of generated queries","Context window and maximum prompt length not documented; unclear how large datasets or complex schemas are handled"],"requires":["AWS account with SageMaker and Amazon Q access","Data sources registered in DataZone or accessible via S3/Redshift","IAM permissions for data source access","SageMaker notebook or IDE with Amazon Q integration"],"input_types":["Natural language prompts","Data source references (S3 paths, Redshift table names, DataZone asset IDs)"],"output_types":["Python training code (PyTorch, TensorFlow, scikit-learn)","SQL queries","Data pipeline definitions","Data discovery results (matching tables/datasets)"],"categories":["code-generation-editing","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_6","uri":"capability://memory.knowledge.sagemaker.catalog.ai.data.asset.governance.and.discovery","name":"sagemaker catalog: ai/data asset governance and discovery","description":"Centralized registry built on Amazon DataZone that enables teams to register, catalog, and discover ML models, datasets, and data pipelines with metadata, lineage, and access controls. Assets are tagged with business context (owner, use case, quality metrics), searchable by natural language queries, and governed through approval workflows. Integrates with SageMaker training and deployment to track model lineage back to source datasets and training configurations.","intents":["I want to discover existing models and datasets in my organization without manual searches","I need to govern ML assets with approval workflows and access controls","I want to track model lineage from raw data through training to deployment"],"best_for":["Large organizations with multiple ML teams needing centralized asset discovery","Regulated industries requiring model governance, audit trails, and access controls","Teams implementing data mesh or federated ML architectures"],"limitations":["Catalog indexing and search latency not documented; no SLA for asset discoverability","Approval workflow customization not detailed; unclear how complex governance policies are enforced","Lineage tracking limited to SageMaker-native components; no support for external training or data pipeline tools","Asset metadata schema not documented; unclear what fields are required vs. optional","Search capabilities limited to DataZone integration; no full-text search or advanced query syntax documented","Integration with external data catalogs (Collibra, Alation) not mentioned; catalog is AWS-specific"],"requires":["AWS account with SageMaker and Amazon DataZone access","Assets registered in DataZone (models, datasets, pipelines)","IAM roles with DataZone governance permissions","Metadata tagging strategy for asset organization"],"input_types":["Model artifacts (S3 objects)","Dataset references (S3, Redshift)","Asset metadata (tags, descriptions, owners)","Approval workflow definitions"],"output_types":["Asset catalog (searchable registry)","Lineage graphs (data → training → model → deployment)","Access control policies","Audit logs"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_7","uri":"capability://memory.knowledge.feature.store.centralized.feature.management.and.serving","name":"feature store: centralized feature management and serving","description":"Managed feature repository that stores pre-computed features in online (low-latency) and offline (batch) storage, enabling ML teams to define features once and reuse across training and inference. Features are organized into feature groups with schemas, versioning, and lineage tracking. Training jobs and inference endpoints can fetch features by entity ID without writing custom data loading code. Supports feature transformations and point-in-time joins for training data consistency.","intents":["I want to define features once and reuse them across multiple models without duplicating feature engineering code","I need low-latency feature retrieval for real-time inference without maintaining separate feature databases","I want to ensure training and inference use consistent feature definitions and versions"],"best_for":["Organizations with multiple ML teams sharing common features (customer, product, transaction features)","Teams building real-time ML systems requiring sub-100ms feature retrieval","Regulated industries needing feature lineage and versioning for audit trails"],"limitations":["Feature Store architecture and storage backend not documented; unclear whether online store uses DynamoDB, ElastiCache, or custom infrastructure","Feature retrieval latency SLAs not provided; no documented p99 latency for online feature serving","Feature transformation capabilities not detailed; unclear what operations are supported (aggregations, joins, custom functions)","Point-in-time join implementation not documented; no clarity on how historical feature consistency is maintained","Scaling limits and throughput not specified; no documented QPS or concurrent request limits","Integration with external feature stores (Tecton, Feast) not mentioned; Feature Store is AWS-specific"],"requires":["AWS account with SageMaker Feature Store access","Feature definitions in SageMaker format (schema, entity key, feature names)","Data source for feature ingestion (S3, Redshift, streaming)","IAM permissions for feature store read/write"],"input_types":["Feature group definitions (schema, entity key)","Feature data (batch or streaming)","Feature retrieval requests (entity IDs)"],"output_types":["Feature vectors (for training)","Feature values (for inference)","Feature metadata and lineage"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_8","uri":"capability://memory.knowledge.sagemaker.jumpstart.pre.built.models.and.solution.templates","name":"sagemaker jumpstart: pre-built models and solution templates","description":"Catalog of pre-trained foundation models and industry-specific ML solution templates that can be deployed with minimal configuration. Models include computer vision, NLP, and time-series models from AWS and third-party providers. Solutions are packaged with training notebooks, deployment code, and example datasets. Users can fine-tune pre-trained models on custom data or deploy them directly to endpoints.","intents":["I want to use a pre-trained model without training from scratch","I need a starting template for a common ML use case (sentiment analysis, object detection, demand forecasting)","I want to fine-tune a foundation model on my custom dataset quickly"],"best_for":["Teams with limited ML expertise wanting to leverage pre-trained models","Organizations needing rapid prototyping of ML solutions","Practitioners fine-tuning foundation models for domain-specific tasks"],"limitations":["Catalog size and model selection not documented; unclear how many models are available or update frequency","Fine-tuning capabilities and supported model architectures not detailed; unclear which models support parameter-efficient tuning","Model licensing and commercial use restrictions not documented; unclear if models can be used in production without additional licensing","Pre-trained model performance benchmarks not provided; no documented accuracy/latency metrics on standard datasets","Customization limitations not specified; unclear how much model architecture modification is supported","Model versioning and update strategy not documented; no clarity on how pre-trained models are updated or deprecated"],"requires":["AWS account with SageMaker access","Custom dataset for fine-tuning (optional; pre-trained models can be deployed directly)","Compute resources for fine-tuning (instance type and duration depend on model size)"],"input_types":["Pre-trained model selection (from JumpStart catalog)","Custom training data (for fine-tuning)","Hyperparameter overrides"],"output_types":["Fine-tuned model artifacts","Deployed inference endpoints","Training notebooks and example code"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__cap_9","uri":"capability://planning.reasoning.automatic.model.evaluation.and.comparison","name":"automatic model evaluation and comparison","description":"Evaluates trained models against user-defined metrics (accuracy, precision, recall, F1, custom metrics) and compares performance across model versions or hyperparameter configurations. Evaluation can be triggered automatically after training or run on-demand against holdout test sets. Results are visualized in dashboards and can be used to gate model promotion (e.g., deploy only if accuracy improves by >1%).","intents":["I want to automatically evaluate model performance after training without writing evaluation code","I need to compare multiple model versions and select the best performer","I want to enforce quality gates that prevent deploying models that don't meet performance thresholds"],"best_for":["ML teams implementing automated model validation in MLOps pipelines","Organizations requiring reproducible model evaluation with audit trails","Teams needing performance dashboards for model monitoring and comparison"],"limitations":["Supported metrics and evaluation frameworks not documented; unclear which metrics are built-in vs. custom","Custom metric implementation not detailed; no clarity on how to define domain-specific evaluation logic","Evaluation dataset handling not specified; unclear how train/validation/test splits are managed","Visualization and dashboard capabilities not documented; no details on available charts or drill-down options","Comparison logic for model selection not detailed; unclear how ties are broken or multi-objective optimization is handled","Integration with model registry and deployment gates not documented; unclear how evaluation results trigger promotion decisions"],"requires":["Trained model artifacts","Test/evaluation dataset","Metric definitions (built-in or custom)","SageMaker evaluation job configuration"],"input_types":["Model artifacts (S3 objects)","Test data (S3 CSV/JSON/Parquet)","Metric definitions (JSON or Python functions)","Comparison criteria (thresholds, weights)"],"output_types":["Evaluation metrics (accuracy, precision, recall, F1, custom)","Model comparison reports","Evaluation dashboards","Pass/fail decisions for deployment gates"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-sagemaker__headline","uri":"capability://model.training.fully.managed.machine.learning.platform","name":"fully managed machine learning platform","description":"AWS SageMaker is a fully managed machine learning service that provides integrated tools for building, training, and deploying machine learning models at scale, leveraging AWS infrastructure.","intents":["best managed machine learning platform","machine learning service for model deployment","AWS service for training ML models","automated model tuning solutions","MLOps platform for scalable AI"],"best_for":["data scientists","ML engineers","enterprise AI projects"],"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":["AWS account with SageMaker service access","IAM permissions for S3 and Redshift access","Web browser for notebook UI access","AWS account with SageMaker training permissions","Training data in S3 or accessible via Redshift","Training script compatible with SageMaker training container (Python, R, or custom Docker image)","IAM role with S3 read/write and CloudWatch logging permissions","Multiple trained model artifacts in S3","Multi-model endpoint configuration (model names, versions, traffic weights)","SageMaker inference container supporting multi-model loading"],"failure_modes":["Notebooks are AWS-hosted only; no option for local execution or hybrid deployment","Amazon Q assistant capabilities limited to AWS-native data sources and SageMaker APIs","Notebook state tied to AWS account; migration to other platforms requires manual export","Serverless notebook option has unknown cold-start latency and scaling characteristics","Specific GPU/CPU instance types and availability not documented; requires AWS pricing calculator to determine costs","Automatic Model Tuning adds latency for Bayesian optimization; no documented SLA for tuning job completion time","Training job logs and metrics stored in CloudWatch; no built-in experiment tracking UI (requires third-party tools or manual CloudWatch queries)","No documented support for custom distributed training frameworks beyond PyTorch/TensorFlow; custom training code must conform to SageMaker training container contract","Hyperparameter tuning limited to numeric and categorical parameters; no support for conditional hyperparameter dependencies","Model loading and invocation latency not documented; unclear if model switching adds latency","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":"2026-05-24T12:16:21.013Z","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=aws-sagemaker","compare_url":"https://unfragile.ai/compare?artifact=aws-sagemaker"}},"signature":"YrzZpZiCJ90+UwSEostaAjKqOP/5Nn6LVUqioT1wkxtcjKUhFU0jZwdzf+IZA6WTl+9ai7rpKFyespiPpMgyAQ==","signedAt":"2026-06-21T18:16:55.289Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aws-sagemaker","artifact":"https://unfragile.ai/aws-sagemaker","verify":"https://unfragile.ai/api/v1/verify?slug=aws-sagemaker","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"}}