{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"mlrun","slug":"mlrun","name":"MLRun","type":"framework","url":"https://www.mlrun.org","page_url":"https://unfragile.ai/mlrun","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"mlrun__cap_0","uri":"capability://automation.workflow.kubernetes.native.serverless.function.orchestration.with.nuclio.integration","name":"kubernetes-native serverless function orchestration with nuclio integration","description":"MLRun abstracts Kubernetes complexity by wrapping serverless function execution through Nuclio, enabling developers to define ML workloads (training, preprocessing, inference) as containerized functions that auto-scale on Kubernetes clusters. 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Each pipeline step is tracked with input/output artifacts, parameters, and metrics; the system auto-generates lineage graphs showing data flow and model provenance across experiments, enabling reproducibility and audit trails without manual logging.","intents":["I want to define multi-stage ML workflows once and run them repeatedly with different parameters","I need to track which data, code, and hyperparameters produced each model version","I want to automatically trigger retraining when upstream data changes","I need to compare experiments side-by-side to identify best-performing configurations"],"best_for":["data science teams managing multiple concurrent experiments","ML engineers building reproducible training pipelines","organizations requiring model lineage for compliance/audit"],"limitations":["Pipeline syntax is MLRun-specific; switching to other orchestrators requires rewriting","Lineage tracking overhead not quantified; may impact performance on high-frequency pipelines","Experiment comparison UI/API details not documented in provided content","No built-in support for conditional branching or dynamic DAG generation mentioned"],"requires":["MLRun SDK installed (Python)","Kubernetes cluster for execution backend","Metadata storage backend (details not specified)"],"input_types":["Python functions","YAML pipeline definitions","Hyperparameter grids","Data artifact references"],"output_types":["Experiment metadata (parameters, metrics, artifacts)","Lineage graphs (DAG visualization)","Model artifacts and checkpoints","Execution logs and performance metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlrun__cap_10","uri":"capability://memory.knowledge.collaborative.experiment.management.with.team.wide.visibility","name":"collaborative experiment management with team-wide visibility","description":"MLRun provides a centralized experiment tracking system where data scientists and ML engineers can log experiments, compare results, and share findings across teams. Experiments are stored in a shared metadata repository with versioning, allowing team members to view all experiments, filter by parameters/metrics, and reproduce results from any experiment; the system supports experiment annotations, comments, and approval workflows for model promotion without requiring external collaboration tools.","intents":["I want to see all experiments run by my team and compare their results side-by-side","I need to reproduce an experiment from 3 months ago to debug a regression","I want to annotate experiments with notes and share findings with teammates","I need to track which experiments led to production models and why"],"best_for":["data science teams with multiple concurrent experiments","organizations needing experiment governance and audit trails","teams collaborating across time zones or locations"],"limitations":["Collaboration features (comments, approvals) not detailed in documentation","Experiment search and filtering capabilities not specified","Access control and permission management not documented","Scalability limits for experiment count not provided"],"requires":["MLRun SDK (Python)","Shared metadata storage backend (details unclear)","Team access to MLRun instance"],"input_types":["Experiment parameters (hyperparameters, data paths, etc.)","Metrics (accuracy, loss, custom metrics)","Artifacts (models, plots, logs)","Annotations and comments (text)"],"output_types":["Experiment metadata and comparison views","Experiment lineage and reproducibility information","Collaboration history and annotations","Model promotion recommendations"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlrun__cap_11","uri":"capability://automation.workflow.batch.and.real.time.data.pipeline.execution.with.unified.scheduling","name":"batch and real-time data pipeline execution with unified scheduling","description":"MLRun supports both batch (scheduled, time-based) and real-time (event-driven, streaming) data pipelines through a unified execution model. 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Each artifact is versioned, tagged, and linked to the pipeline/experiment that produced it; the system tracks which artifacts depend on which data versions and code versions, enabling reproducibility and rollback. Users can query the registry by artifact type, version, or metadata, and retrieve specific versions for retraining or serving without manual file management.","intents":["I want to retrieve the exact dataset version used to train a model from 6 months ago","I need to roll back a model to a previous version and understand what changed","I want to track which models depend on a specific dataset version","I need to clean up old artifact versions and manage storage costs"],"best_for":["teams managing many model versions and datasets","organizations with strict reproducibility and audit requirements","data scientists needing quick access to historical artifacts"],"limitations":["Artifact storage backend and cost implications not documented","Garbage collection and retention policies not specified","Artifact size limits and performance for large models unclear","Cross-project artifact sharing not mentioned"],"requires":["MLRun SDK (Python)","Artifact storage backend (S3, GCS, local — details unclear)","Metadata storage (details unclear)"],"input_types":["Model artifacts (any format)","Dataset files (any format)","Pipeline outputs (logs, metrics, etc.)"],"output_types":["Artifact metadata (version, tags, dependencies)","Artifact retrieval (download or reference)","Dependency graphs (artifact lineage)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlrun__cap_2","uri":"capability://data.processing.analysis.built.in.feature.store.with.real.time.and.batch.serving","name":"built-in feature store with real-time and batch serving","description":"MLRun includes a native feature store that manages feature definitions, transformations, and storage across batch and real-time contexts. Features are defined declaratively, computed from raw data via transformations, and cached in configurable backends (in-memory, Redis, database); the system serves features to training pipelines and inference endpoints with automatic versioning and point-in-time correctness for training/serving consistency.","intents":["I want to define features once and reuse them across training and serving without duplication","I need to serve pre-computed features to real-time inference endpoints with sub-100ms latency","I want to ensure training data and serving features are computed from the same logic to avoid training/serving skew","I need to version features and roll back to previous versions if a transformation breaks"],"best_for":["teams building real-time ML systems with strict latency requirements","organizations with many models sharing common features","enterprises needing feature governance and versioning"],"limitations":["Feature store backend options and performance characteristics not documented","Real-time serving latency SLAs not specified","No mention of feature monitoring or drift detection","Scalability limits for feature count and QPS not provided"],"requires":["MLRun SDK (Python)","Feature store backend (Redis, database, or in-memory — specifics unclear)","Data source connectors (S3, GCS, database — specific support unclear)"],"input_types":["Raw data (batch or streaming)","Feature transformation code (Python functions)","Feature definitions (YAML or SDK)"],"output_types":["Feature vectors (numerical arrays)","Feature metadata (schema, versioning)","Training datasets (with point-in-time features)","Real-time feature responses (JSON/protobuf)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlrun__cap_3","uri":"capability://automation.workflow.real.time.model.serving.with.automatic.scaling.and.canary.deployments","name":"real-time model serving with automatic scaling and canary deployments","description":"MLRun provides a serving framework that deploys trained models as HTTP/gRPC endpoints on Kubernetes with automatic scaling based on request volume. Models are wrapped in serving classes that handle preprocessing, inference, and postprocessing; the system supports canary deployments (gradual traffic shifting) and A/B testing without manual load balancer configuration, with built-in monitoring of latency, throughput, and model performance metrics.","intents":["I want to deploy a trained model as a REST API that scales automatically with traffic","I need to test a new model version on 10% of traffic before rolling out to 100%","I want to monitor model inference latency and error rates in real-time","I need to serve multiple model versions simultaneously for A/B testing"],"best_for":["teams deploying ML models to production with high availability requirements","organizations needing gradual rollout strategies to minimize risk","data science teams without dedicated MLOps/DevOps resources"],"limitations":["Serving framework details (preprocessing hooks, batching support) not documented","Canary deployment granularity and traffic shifting algorithms not specified","Cold start latency for model loading not addressed","No mention of model caching, quantization, or inference optimization"],"requires":["Trained model artifact (format support unclear)","Kubernetes cluster with Nuclio runtime","Serving class definition (Python code)","MLRun SDK"],"input_types":["Trained model files","Serving class code (Python)","Request data (JSON, protobuf, or raw bytes)"],"output_types":["Inference results (JSON, protobuf, or custom format)","Serving metrics (latency, throughput, error rate)","Deployment status and logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlrun__cap_4","uri":"capability://automation.workflow.multi.framework.model.training.with.gpu.provisioning.and.distributed.execution","name":"multi-framework model training with gpu provisioning and distributed execution","description":"MLRun abstracts training execution across multiple ML frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost, etc.) by wrapping training code in a standardized function interface. The system automatically provisions GPUs from the Kubernetes cluster, distributes training across multiple nodes using framework-native distributed training (Horovod, PyTorch DDP), and manages resource allocation without requiring users to write distributed training code or GPU management logic.","intents":["I want to run training jobs on GPUs without manually configuring CUDA or distributed training","I need to scale training from single-GPU to multi-GPU/multi-node without rewriting code","I want to train models using different frameworks (PyTorch, TensorFlow) with the same orchestration","I need to automatically save checkpoints and resume training from interruptions"],"best_for":["data scientists training large models on GPU clusters","teams using multiple ML frameworks and needing unified orchestration","organizations with expensive GPU resources needing efficient utilization"],"limitations":["Distributed training framework support (Horovod, DDP) not explicitly documented","GPU type selection and allocation strategy not specified","Checkpoint management and resumption logic not detailed","Training performance overhead from MLRun abstraction not quantified"],"requires":["Kubernetes cluster with GPU nodes (NVIDIA CUDA, driver version unclear)","ML framework installed (TensorFlow, PyTorch, scikit-learn, etc.)","MLRun SDK (Python)","Training code wrapped in MLRun function interface"],"input_types":["Python training functions","Training data (local paths, S3, GCS, etc.)","Hyperparameters (dict or grid)","Model architecture definitions"],"output_types":["Trained model artifacts","Training metrics (loss, accuracy, etc.)","Checkpoints and intermediate artifacts","Training logs and execution metadata"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlrun__cap_5","uri":"capability://automation.workflow.hugging.face.model.integration.for.llm.deployment.and.fine.tuning","name":"hugging face model integration for llm deployment and fine-tuning","description":"MLRun provides native integration with Hugging Face model hub, enabling direct loading of pre-trained LLMs and fine-tuning them within MLRun pipelines. Models are downloaded from Hugging Face, fine-tuned using MLRun's distributed training infrastructure with GPU support, and deployed as serving endpoints; the system handles model versioning, caching, and compatibility with Hugging Face tokenizers and inference libraries without custom integration code.","intents":["I want to fine-tune a Hugging Face LLM on my custom data without writing distributed training code","I need to deploy a Hugging Face model as a real-time inference endpoint with auto-scaling","I want to compare multiple Hugging Face models on my task and track their performance","I need to version and manage multiple fine-tuned variants of the same base model"],"best_for":["teams building LLM applications without deep ML infrastructure expertise","organizations fine-tuning open-source models for domain-specific tasks","data scientists experimenting with multiple Hugging Face models"],"limitations":["Fine-tuning approach (full vs. LoRA, quantization) not specified","Supported model sizes and memory requirements not documented","Inference optimization (quantization, distillation) not mentioned","Hugging Face API rate limits and caching strategy unclear"],"requires":["Hugging Face account (optional, for private models)","GPU cluster for fine-tuning (VRAM requirements unclear)","MLRun SDK (Python)","Transformers library (version not specified)"],"input_types":["Hugging Face model identifiers (string)","Fine-tuning datasets (CSV, JSON, Parquet, or HuggingFace datasets)","Fine-tuning hyperparameters (learning rate, batch size, etc.)","Inference prompts (text)"],"output_types":["Fine-tuned model artifacts","Model performance metrics (loss, BLEU, ROUGE, etc.)","Inference results (text completions)","Model metadata and versioning"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlrun__cap_6","uri":"capability://automation.workflow.nvidia.nim.inference.optimization.for.accelerated.model.serving","name":"nvidia nim inference optimization for accelerated model serving","description":"MLRun integrates NVIDIA NIM (NVIDIA Inference Microservices) to optimize model inference performance through quantization, batching, and GPU-accelerated kernels. Models deployed via MLRun can be automatically optimized with NIM, reducing latency and increasing throughput for inference endpoints without requiring manual optimization code; the system handles NIM container orchestration on Kubernetes and metric collection for performance monitoring.","intents":["I want to reduce model inference latency by 50%+ without retraining or changing the model","I need to serve more inference requests per GPU by batching and optimizing kernels","I want to deploy quantized models (INT8, FP16) with minimal accuracy loss","I need to monitor inference performance (latency, throughput, GPU utilization) in real-time"],"best_for":["teams serving high-throughput inference workloads with latency constraints","organizations with NVIDIA GPU infrastructure (A100, H100, etc.)","enterprises needing inference cost optimization"],"limitations":["Supported model architectures and quantization methods not documented","Accuracy impact of quantization not specified","NIM licensing and cost implications unclear","Fallback behavior if NIM optimization fails not addressed"],"requires":["NVIDIA GPU (A100, H100, or compatible)","NVIDIA NIM license/subscription (cost and terms unclear)","Kubernetes cluster with NVIDIA GPU operator","MLRun SDK (Python)"],"input_types":["Trained model artifacts","Quantization configuration (INT8, FP16, etc.)","Inference requests (batch size, format)"],"output_types":["Optimized model artifacts","Inference results (same format as original model)","Performance metrics (latency, throughput, GPU utilization)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlrun__cap_7","uri":"capability://data.processing.analysis.automated.data.validation.and.quality.monitoring.in.pipelines","name":"automated data validation and quality monitoring in pipelines","description":"MLRun includes data validation capabilities that check data quality, schema compliance, and statistical properties at each pipeline stage. Validation rules are defined declaratively (schema, value ranges, null checks, statistical thresholds), executed automatically during pipeline runs, and trigger alerts or pipeline halts if data quality degrades; the system tracks data quality metrics over time to detect drift or anomalies without manual data inspection.","intents":["I want to automatically reject training data that doesn't match expected schema or value ranges","I need to detect when input data distribution changes significantly (data drift)","I want to ensure data quality gates are enforced before model training starts","I need to track data quality metrics over time and alert on degradation"],"best_for":["teams managing large-scale data pipelines with quality concerns","organizations needing data governance and compliance","data engineers building robust ML pipelines"],"limitations":["Validation rule types and expressiveness not documented","Data drift detection algorithms and thresholds not specified","Performance impact of validation on pipeline throughput unclear","Integration with external data quality tools (Great Expectations, etc.) not mentioned"],"requires":["MLRun SDK (Python)","Data validation rules defined (schema, thresholds, etc.)","Data source connectors (S3, database, etc.)"],"input_types":["Raw data (batch or streaming)","Validation rule definitions (YAML or SDK)","Historical data quality metrics (for drift detection)"],"output_types":["Validation results (pass/fail, error details)","Data quality metrics (null rate, value distribution, etc.)","Alerts and notifications","Data quality reports and dashboards"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlrun__cap_8","uri":"capability://automation.workflow.model.monitoring.and.automated.retraining.triggers","name":"model monitoring and automated retraining triggers","description":"MLRun provides production model monitoring that tracks inference performance metrics (latency, error rate, prediction distribution) and data quality in real-time. The system automatically detects performance degradation or data drift and triggers retraining pipelines without manual intervention; monitoring rules are defined declaratively (e.g., 'retrain if accuracy drops below 90%'), and retraining jobs are scheduled and executed using the same pipeline infrastructure.","intents":["I want to automatically detect when a deployed model's performance degrades in production","I need to trigger retraining when input data distribution changes significantly","I want to monitor model inference latency and alert if it exceeds SLA","I need to automatically roll back to a previous model version if performance drops"],"best_for":["teams managing models in production with SLA requirements","organizations needing continuous model improvement without manual intervention","enterprises with compliance requirements for model monitoring"],"limitations":["Performance degradation detection algorithms not specified","Retraining trigger thresholds and tuning guidance not provided","Rollback strategy and safety checks not documented","Monitoring latency and overhead not quantified"],"requires":["Deployed model serving endpoint","MLRun SDK (Python)","Monitoring rules defined (thresholds, metrics, etc.)","Retraining pipeline defined"],"input_types":["Inference requests and responses","Ground truth labels (for accuracy monitoring)","Monitoring rule definitions (YAML or SDK)"],"output_types":["Monitoring metrics (accuracy, latency, data drift)","Alerts and notifications","Retraining job status and results","Model rollback decisions and logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlrun__cap_9","uri":"capability://automation.workflow.multi.cloud.and.hybrid.deployment.with.infrastructure.abstraction","name":"multi-cloud and hybrid deployment with infrastructure abstraction","description":"MLRun abstracts underlying infrastructure (on-premises Kubernetes, AWS EKS, Google GKE, Azure AKS) through a unified API, enabling ML pipelines to run on any Kubernetes cluster without code changes. 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