{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-ludwig","slug":"ludwig","name":"Ludwig","type":"framework","url":"https://github.com/ludwig-ai/ludwig","page_url":"https://unfragile.ai/ludwig","categories":["frameworks-sdks","app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-ludwig__cap_0","uri":"capability://automation.workflow.declarative.yaml.based.model.configuration.with.hierarchical.schema.validation","name":"declarative yaml-based model configuration with hierarchical schema validation","description":"Ludwig accepts machine learning model definitions as declarative YAML configurations that specify input features, output features, model architecture, and training parameters. The framework validates these configurations against a hierarchical schema system with defaults and type checking, then automatically translates them into executable training pipelines without requiring users to write model definition code. This declarative approach abstracts away PyTorch/TensorFlow boilerplate while maintaining full architectural control.","intents":["Define a custom deep learning model without writing neural network code","Quickly experiment with different feature encodings and model architectures by editing YAML","Validate model configurations before training to catch schema errors early","Share reproducible model definitions across teams via version-controlled YAML files"],"best_for":["ML practitioners who prefer configuration-driven development over imperative code","Teams building multiple similar models with varying feature sets","Non-ML engineers prototyping custom AI models with minimal deep learning knowledge"],"limitations":["Complex custom layers or loss functions require extending the framework with Python code","YAML configuration complexity grows significantly for multi-task learning with many features","Limited IDE support for YAML schema validation compared to programmatic APIs"],"requires":["Python 3.9+","Valid YAML syntax","Understanding of Ludwig's feature types (text, image, numeric, categorical, etc.)"],"input_types":["YAML configuration files","Python dictionaries with configuration structure"],"output_types":["Validated configuration objects","Executable model definition ready for training"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_1","uri":"capability://data.processing.analysis.multi.format.data.preprocessing.with.feature.specific.encoders","name":"multi-format data preprocessing with feature-specific encoders","description":"Ludwig's data processing system automatically handles diverse input formats (CSV, JSON, Parquet, DataFrames) and applies feature-specific preprocessing pipelines based on the declared feature type. Text features use tokenization and embedding, images use resizing and normalization, numeric features use scaling, and categorical features use encoding—all configured declaratively without manual preprocessing code. The system batches processed data efficiently for training and inference.","intents":["Load raw data in multiple formats and automatically prepare it for model training","Apply consistent preprocessing to text, images, numbers, and categorical data without writing custom code","Handle missing values, outliers, and feature scaling automatically based on feature type","Preprocess data once and reuse the fitted preprocessor for inference on new data"],"best_for":["Data scientists building models with heterogeneous feature types (mixed text, images, numbers)","Teams needing reproducible preprocessing that's version-controlled alongside model configs","Practitioners who want to avoid sklearn pipeline boilerplate for feature engineering"],"limitations":["Custom preprocessing logic requires writing Python code outside the declarative config","Preprocessing is tightly coupled to the model—cannot easily reuse preprocessors across different models","Limited support for streaming data; designed for batch preprocessing of complete datasets"],"requires":["Python 3.9+","Input data in CSV, JSON, Parquet, or pandas DataFrame format","Feature type declarations in configuration (text, image, numeric, categorical, etc.)"],"input_types":["CSV files","JSON files","Parquet files","pandas DataFrames","raw text, image paths, numeric values"],"output_types":["Preprocessed tensors ready for model training","Fitted preprocessor objects for inference","Batched datasets with configurable batch sizes"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_10","uri":"capability://automation.workflow.mlflow.integration.for.experiment.tracking.and.model.registry","name":"mlflow integration for experiment tracking and model registry","description":"Ludwig integrates with MLflow to automatically log training runs, metrics, hyperparameters, and model artifacts. Users enable MLflow in configuration; Ludwig logs all training details (loss, validation metrics, hyperparameters) to MLflow, registers trained models in the MLflow Model Registry, and enables comparison of multiple training runs. This provides experiment tracking and model versioning without additional code.","intents":["Track training runs and metrics automatically without manual MLflow logging","Compare multiple model configurations and training runs in MLflow UI","Register trained models in MLflow Model Registry for versioning and deployment","Reproduce training runs by accessing logged hyperparameters and artifacts"],"best_for":["Teams using MLflow for experiment tracking and model management","Organizations standardizing on MLflow for ML lifecycle management","Projects requiring reproducible training with full audit trails"],"limitations":["MLflow integration is optional; requires separate MLflow server setup","Custom metrics or artifacts require manual MLflow logging outside Ludwig","Model Registry integration is basic; advanced deployment workflows require custom code","MLflow UI is separate from Ludwig; no integrated dashboard"],"requires":["Python 3.9+","MLflow installed and configured","MLflow tracking server running (local or remote)","Configuration enabling MLflow integration"],"input_types":["Training configuration with MLflow settings (experiment name, tracking URI, etc.)"],"output_types":["MLflow runs with logged metrics, hyperparameters, and artifacts","Registered models in MLflow Model Registry","Model versions with metadata and lineage"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_11","uri":"capability://automation.workflow.model.serving.and.rest.api.deployment.with.automatic.input.output.serialization","name":"model serving and rest api deployment with automatic input/output serialization","description":"Ludwig provides built-in model serving capabilities that expose trained models as REST APIs with automatic input/output serialization. Users call a serve() method or use Ludwig's CLI to start an HTTP server; the server handles request parsing, preprocessing, inference, and response formatting without requiring users to write API code. The server automatically handles multiple input formats and returns predictions in JSON.","intents":["Deploy trained models as REST APIs without writing Flask/FastAPI code","Serve models with automatic input validation and preprocessing","Handle multiple concurrent prediction requests efficiently","Expose model predictions via standard HTTP endpoints"],"best_for":["Practitioners deploying models to production without API development experience","Teams needing quick model serving without custom API development","Projects where automatic input/output serialization is sufficient"],"limitations":["REST API is basic; advanced features (authentication, rate limiting, caching) require custom code","Serving is single-machine; no built-in load balancing or horizontal scaling","No support for streaming predictions or real-time model updates","Performance is limited by single-machine throughput; high-traffic deployments require external load balancing"],"requires":["Python 3.9+","Trained Ludwig model","HTTP server dependencies (Flask or similar)"],"input_types":["HTTP requests with JSON payload containing input features"],"output_types":["JSON responses with predictions and confidence scores"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_12","uri":"capability://data.processing.analysis.visualization.of.training.progress.model.architecture.and.prediction.results","name":"visualization of training progress, model architecture, and prediction results","description":"Ludwig includes visualization tools that generate plots of training loss and metrics over epochs, visualize model architecture as computational graphs, and create confusion matrices and ROC curves for classification tasks. Visualizations are generated automatically during training and evaluation, and can be customized via configuration. This provides quick feedback on model training and performance without writing plotting code.","intents":["Monitor training progress via loss and metric plots without manual visualization code","Understand model architecture visually via computational graph diagrams","Analyze classification performance via confusion matrices and ROC curves","Generate publication-ready plots for model evaluation reports"],"best_for":["Practitioners wanting quick visual feedback on training without plotting code","Teams documenting model performance with automated visualizations","Projects where standard visualizations (loss curves, confusion matrices) are sufficient"],"limitations":["Visualization options are limited to built-in plot types; custom visualizations require external tools","Plots are static; no interactive exploration of training dynamics","Large models produce complex architecture diagrams that are difficult to interpret","Visualization customization (colors, fonts, labels) is limited"],"requires":["Python 3.9+","Matplotlib or similar plotting library","Training or evaluation results to visualize"],"input_types":["Training statistics (loss, metrics per epoch)","Model architecture definition","Evaluation results (predictions, ground truth)"],"output_types":["Loss and metric plots (PNG, PDF)","Model architecture diagrams","Confusion matrices and ROC curves","Feature importance plots (for GBMs)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_13","uri":"capability://code.generation.editing.custom.feature.encoders.and.decoders.via.python.extension","name":"custom feature encoders and decoders via python extension","description":"Ludwig allows users to extend the framework with custom feature encoders and decoders by subclassing base encoder/decoder classes and registering them with Ludwig's feature system. Custom encoders can implement arbitrary neural network architectures for specific feature types, and custom decoders can handle task-specific output transformations. This enables advanced users to add domain-specific feature processing without modifying Ludwig's core code.","intents":["Implement custom feature encoders for domain-specific data types (e.g., graph data, time series)","Create specialized decoders for custom output tasks (e.g., structured prediction)","Extend Ludwig with novel neural network architectures for specific features","Integrate external libraries (e.g., graph neural networks) into Ludwig's pipeline"],"best_for":["Advanced users with deep learning expertise who need custom architectures","Teams building domain-specific models requiring specialized feature processing","Researchers implementing novel encoder/decoder architectures"],"limitations":["Custom encoders/decoders require Python coding and deep learning knowledge","Custom components may not be compatible with distributed training or HPO","Debugging custom encoders requires understanding Ludwig's internal architecture","Custom components are not automatically serialized; require custom save/load logic"],"requires":["Python 3.9+","Deep learning framework knowledge (PyTorch or TensorFlow)","Understanding of Ludwig's encoder/decoder base classes","Ability to subclass and register custom components"],"input_types":["Custom encoder/decoder Python class definitions"],"output_types":["Registered custom encoders/decoders usable in Ludwig configurations"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_2","uri":"capability://code.generation.editing.encoder.combiner.decoder.ecd.architecture.composition.with.pluggable.encoders.and.decoders","name":"encoder-combiner-decoder (ecd) architecture composition with pluggable encoders and decoders","description":"Ludwig implements a modular neural network architecture pattern where input features are encoded independently using feature-specific encoders (e.g., LSTM for text, CNN for images), combined via a configurable combiner layer, and then decoded into task-specific outputs. Each encoder and decoder is pluggable and can be swapped declaratively, allowing users to compose custom architectures by selecting from built-in components without writing neural network code. The ECD pattern naturally supports multi-task learning with different output decoders.","intents":["Build multi-input, multi-output neural networks by composing independent encoders and decoders","Switch between different encoder types (e.g., LSTM vs Transformer for text) via configuration","Train models that predict multiple targets simultaneously with task-specific decoders","Reuse trained encoders across different downstream tasks without retraining"],"best_for":["Teams building multi-task learning models with heterogeneous inputs and outputs","Practitioners experimenting with different encoder architectures without rewriting model code","Projects requiring transfer learning where encoders are frozen and only decoders are fine-tuned"],"limitations":["ECD pattern assumes independence of input encoders—complex cross-feature interactions require custom combiner code","Decoder flexibility is limited to built-in task types; custom output heads require framework extension","Combiner layer is a simple concatenation or projection by default; advanced fusion methods require custom implementation"],"requires":["Python 3.9+","Feature type declarations in configuration","Understanding of ECD architecture pattern (encoders → combiner → decoders)"],"input_types":["Multiple heterogeneous input features (text, image, numeric, categorical)","Configuration specifying encoder types per feature"],"output_types":["Composed neural network model","Predictions for multiple output tasks"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_3","uri":"capability://automation.workflow.unified.model.training.pipeline.with.configurable.optimizers.learning.rates.and.early.stopping","name":"unified model training pipeline with configurable optimizers, learning rates, and early stopping","description":"Ludwig's training system provides a unified pipeline that handles data loading, batching, forward passes, loss computation, backpropagation, and validation—all configured declaratively. Users specify optimizer type, learning rate schedules, batch size, epochs, and early stopping criteria in YAML; Ludwig handles the training loop, gradient updates, and checkpoint management. The Trainer class abstracts backend differences (PyTorch, TensorFlow) and supports distributed training via Ray or Horovod.","intents":["Train a model end-to-end with a single API call, specifying hyperparameters via configuration","Use different optimizers (Adam, SGD, etc.) and learning rate schedules without code changes","Implement early stopping based on validation metrics to prevent overfitting","Resume training from checkpoints if interrupted"],"best_for":["ML practitioners who want to focus on data and features, not training loop implementation","Teams standardizing training procedures across multiple models","Projects requiring reproducible training with fixed random seeds and configuration versioning"],"limitations":["Custom loss functions or training objectives require extending the Trainer class","Limited control over gradient accumulation, mixed precision training, or advanced optimization techniques","Training loop is opaque—debugging convergence issues requires understanding Ludwig's internal training code"],"requires":["Python 3.9+","Training dataset in supported format (CSV, JSON, Parquet, DataFrame)","Valid configuration with input/output features and training parameters"],"input_types":["Training dataset (CSV, JSON, Parquet, DataFrame)","Validation and test datasets (optional)","Configuration specifying optimizer, learning rate, batch size, epochs"],"output_types":["Trained model weights and architecture","Training statistics (loss, metrics per epoch)","Checkpoints at specified intervals","TrainingResults object with preprocessed data and output directory"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_4","uri":"capability://planning.reasoning.hyperparameter.optimization.with.grid.search.random.search.and.bayesian.optimization","name":"hyperparameter optimization with grid search, random search, and bayesian optimization","description":"Ludwig integrates hyperparameter optimization (HPO) capabilities that automatically search over specified parameter ranges using grid search, random search, or Bayesian optimization strategies. Users define a search space in configuration (e.g., learning rate ranges, layer sizes), and Ludwig trains multiple model variants in parallel, evaluates them on validation data, and returns the best configuration. HPO is integrated with the training pipeline and supports distributed execution via Ray.","intents":["Automatically find optimal hyperparameters without manual trial-and-error","Run multiple training jobs in parallel to explore the hyperparameter space efficiently","Compare different model architectures and training configurations systematically","Save the best hyperparameter configuration for production deployment"],"best_for":["Teams with computational resources to run multiple training jobs in parallel","Practitioners optimizing models for specific datasets where manual tuning is time-consuming","Projects where hyperparameter sensitivity is high and systematic search is justified"],"limitations":["Bayesian optimization requires significant computational overhead and is slower for small search spaces","HPO is limited to hyperparameters declared in configuration; custom model parameters require code changes","No support for multi-objective optimization (e.g., accuracy vs latency trade-offs)","HPO results are dataset-specific and may not transfer to new data distributions"],"requires":["Python 3.9+","Ray cluster for distributed HPO (optional but recommended for large search spaces)","Configuration with hyperparameter search space definitions","Sufficient computational resources to train multiple models in parallel"],"input_types":["Configuration with hyperparameter ranges (learning rate, batch size, layer sizes, etc.)","Training, validation, and test datasets","Search strategy specification (grid, random, or Bayesian)"],"output_types":["Best hyperparameter configuration found","Training results for all evaluated configurations","Comparison metrics (validation loss, accuracy, etc.) across configurations"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_5","uri":"capability://automation.workflow.distributed.training.across.multiple.gpus.and.machines.via.ray.and.horovod.backends","name":"distributed training across multiple gpus and machines via ray and horovod backends","description":"Ludwig abstracts distributed training complexity by supporting multiple backends (Ray, Horovod) that handle data parallelism, gradient synchronization, and communication across GPUs and machines. Users specify the backend and number of workers in configuration; Ludwig automatically distributes the training loop, handles gradient aggregation, and manages worker communication. This enables scaling to large datasets and models without modifying training code.","intents":["Train large models on multiple GPUs without writing distributed training code","Scale training across multiple machines in a cluster","Use data parallelism to reduce training time for large datasets","Switch between Ray and Horovod backends without changing model or training code"],"best_for":["Teams with access to multi-GPU or multi-machine clusters","Projects training large models (LLMs, large vision models) that require distributed training","Organizations standardizing on Ray or Horovod for distributed ML workloads"],"limitations":["Distributed training adds communication overhead (~10-30% depending on network bandwidth)","Debugging distributed training issues is complex; requires understanding of backend-specific logging","Not all custom layers or loss functions are compatible with distributed training","Requires cluster setup and management (Ray cluster, Horovod environment)"],"requires":["Python 3.9+","Ray cluster (for Ray backend) or Horovod installation (for Horovod backend)","Multi-GPU setup or multi-machine cluster","Configuration specifying backend and number of workers"],"input_types":["Training dataset (distributed across workers)","Configuration with backend specification (Ray or Horovod) and worker count"],"output_types":["Trained model (aggregated from distributed workers)","Training statistics with distributed metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_6","uri":"capability://automation.workflow.batch.prediction.on.new.data.with.preprocessing.reuse.and.output.formatting","name":"batch prediction on new data with preprocessing reuse and output formatting","description":"Ludwig's predict() method applies a trained model to new data while automatically reusing the fitted preprocessor from training. The method handles data loading, preprocessing, batching, inference, and output formatting—all without requiring users to manually apply the same preprocessing steps. Predictions can be returned as DataFrames, JSON, or other formats, and include confidence scores or probabilities for classification tasks.","intents":["Make predictions on new data using a trained model without manually reapplying preprocessing","Batch predict on large datasets efficiently with automatic batching","Get predictions in multiple output formats (DataFrame, JSON, CSV)","Include confidence scores or probabilities in predictions for uncertainty quantification"],"best_for":["Practitioners deploying trained models to production for batch inference","Teams needing consistent preprocessing between training and inference","Projects where prediction output formatting is important (JSON APIs, CSV exports)"],"limitations":["Prediction is batch-only; no streaming or online prediction support","Preprocessing must be identical to training; cannot apply different preprocessing at inference time","Large batch predictions may require significant memory; no built-in memory-efficient streaming","Output formatting options are limited to built-in formats; custom output requires post-processing"],"requires":["Python 3.9+","Trained Ludwig model (loaded via LudwigModel.load())","New data in same format as training data (CSV, JSON, Parquet, DataFrame)"],"input_types":["New dataset (CSV, JSON, Parquet, DataFrame)","Trained model with fitted preprocessor"],"output_types":["Predictions as DataFrame","Predictions as JSON","Predictions with confidence scores/probabilities","Predictions in CSV format"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_7","uri":"capability://data.processing.analysis.model.evaluation.with.multiple.metrics.and.cross.validation.support","name":"model evaluation with multiple metrics and cross-validation support","description":"Ludwig's evaluate() method computes task-specific metrics (accuracy, F1, RMSE, etc.) on test data and supports cross-validation to estimate model generalization. The framework automatically selects appropriate metrics based on the output task type (classification, regression, etc.) and returns detailed evaluation results including per-class metrics for multi-class problems. Evaluation integrates with the training pipeline and can be run on any dataset.","intents":["Evaluate trained models on test data using task-appropriate metrics","Estimate model generalization via cross-validation without manual fold splitting","Compare models trained with different configurations using consistent metrics","Get detailed evaluation reports including per-class metrics for classification tasks"],"best_for":["ML practitioners validating model performance before deployment","Teams comparing multiple model configurations systematically","Projects requiring cross-validation for robust generalization estimates"],"limitations":["Metrics are limited to built-in task-specific metrics; custom metrics require framework extension","Cross-validation is computationally expensive (requires training K models for K-fold CV)","Evaluation results are not automatically logged to experiment tracking systems (requires MLflow integration)","No support for stratified cross-validation or custom fold splitting strategies"],"requires":["Python 3.9+","Trained Ludwig model","Test dataset in supported format (CSV, JSON, Parquet, DataFrame)"],"input_types":["Test dataset (CSV, JSON, Parquet, DataFrame)","Trained model"],"output_types":["Task-specific metrics (accuracy, F1, RMSE, etc.)","Per-class metrics for classification","Cross-validation results (if enabled)","Evaluation report as DataFrame or dictionary"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_8","uri":"capability://code.generation.editing.llm.fine.tuning.with.lora.and.parameter.efficient.adaptation","name":"llm fine-tuning with lora and parameter-efficient adaptation","description":"Ludwig supports fine-tuning pre-trained Large Language Models (LLMs) using parameter-efficient methods like Low-Rank Adaptation (LoRA), which trains only a small fraction of parameters while keeping the base model frozen. Users specify the base LLM (e.g., from Hugging Face), the fine-tuning method, and the task in configuration; Ludwig handles loading the model, applying LoRA adapters, and training on custom data. This enables fine-tuning large models on consumer hardware.","intents":["Fine-tune pre-trained LLMs on custom data without full model training","Use parameter-efficient methods (LoRA) to reduce memory and compute requirements","Adapt LLMs to domain-specific tasks (classification, generation, etc.) with minimal data","Deploy fine-tuned LLMs efficiently by storing only LoRA weights instead of full model"],"best_for":["Teams adapting LLMs to domain-specific tasks with limited computational resources","Practitioners fine-tuning models on consumer GPUs using LoRA or similar methods","Projects requiring rapid LLM adaptation to new domains or tasks"],"limitations":["LoRA fine-tuning is less effective than full fine-tuning for significant domain shifts","Limited to LoRA and similar parameter-efficient methods; full fine-tuning requires custom code","Requires Hugging Face model compatibility; custom or proprietary LLMs may not be supported","Fine-tuned models inherit biases and limitations of the base LLM"],"requires":["Python 3.9+","Pre-trained LLM from Hugging Face Model Hub","GPU with sufficient VRAM for LoRA fine-tuning (typically 8GB+)","Configuration specifying base LLM, fine-tuning method, and task"],"input_types":["Pre-trained LLM identifier (e.g., 'meta-llama/Llama-2-7b')","Fine-tuning dataset (text pairs for classification, prompts for generation, etc.)","Configuration with LoRA rank, alpha, and other hyperparameters"],"output_types":["Fine-tuned LLM with LoRA adapters","LoRA weight files (much smaller than full model)","Fine-tuning metrics and loss curves"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ludwig__cap_9","uri":"capability://automation.workflow.gradient.boosted.machine.gbm.training.as.alternative.to.neural.networks","name":"gradient boosted machine (gbm) training as alternative to neural networks","description":"Ludwig supports training Gradient Boosted Machines (GBMs) using XGBoost or LightGBM as an alternative to neural networks, configured declaratively alongside neural network models. Users specify 'gbm' as the model type in configuration; Ludwig handles feature preprocessing, GBM training, and hyperparameter tuning. This enables practitioners to compare neural networks and GBMs on the same dataset without switching frameworks.","intents":["Train GBM models using the same declarative configuration as neural networks","Compare neural networks and GBMs on the same dataset to determine the best approach","Use GBMs for tabular data where they often outperform neural networks","Leverage GBM interpretability (feature importance) alongside neural network predictions"],"best_for":["Practitioners working with tabular data where GBMs are competitive or superior","Teams comparing multiple model types (neural networks, GBMs) systematically","Projects requiring interpretable models where GBM feature importance is valuable"],"limitations":["GBM support is limited to tabular data; image and text features require neural network encoders","GBM hyperparameter tuning is less automated than neural network HPO","Multi-task learning with GBMs is not supported; each task requires a separate model","GBM inference is slower than neural networks for real-time applications"],"requires":["Python 3.9+","XGBoost or LightGBM installed","Tabular data (numeric and categorical features)","Configuration specifying 'gbm' as model type"],"input_types":["Tabular dataset with numeric and categorical features (CSV, JSON, Parquet, DataFrame)"],"output_types":["Trained GBM model","Feature importance scores","Predictions with confidence scores"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Valid YAML syntax","Understanding of Ludwig's feature types (text, image, numeric, categorical, etc.)","Input data in CSV, JSON, Parquet, or pandas DataFrame format","Feature type declarations in configuration (text, image, numeric, categorical, etc.)","MLflow installed and configured","MLflow tracking server running (local or remote)","Configuration enabling MLflow integration","Trained Ludwig model","HTTP server dependencies (Flask or similar)"],"failure_modes":["Complex custom layers or loss functions require extending the framework with Python code","YAML configuration complexity grows significantly for multi-task learning with many features","Limited IDE support for YAML schema validation compared to programmatic APIs","Custom preprocessing logic requires writing Python code outside the declarative config","Preprocessing is tightly coupled to the model—cannot easily reuse preprocessors across different models","Limited support for streaming data; designed for batch preprocessing of complete datasets","MLflow integration is optional; requires separate MLflow server setup","Custom metrics or artifacts require manual MLflow logging outside Ludwig","Model Registry integration is basic; advanced deployment workflows require custom code","MLflow UI is separate from Ludwig; no integrated dashboard","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=ludwig","compare_url":"https://unfragile.ai/compare?artifact=ludwig"}},"signature":"YV1qa/3q0wib4rsX7YS0hEWsncHN7bdWDRIMO9xC5AYxdSjS34lT5SuvTCktHq8XEn3VQKJhy7TtmZTZn2RlAQ==","signedAt":"2026-06-22T13:11:04.046Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ludwig","artifact":"https://unfragile.ai/ludwig","verify":"https://unfragile.ai/api/v1/verify?slug=ludwig","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"}}