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Documentation Index

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Input data types supported by Fiddler models. This enum defines the different types of input data that models can process. The input type determines how Fiddler handles data preprocessing, validation, and monitoring for the model.

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Examples

Defining model input type during onboarding: 
# Tabular data model (traditional ML)
model = fdl.Model.from_data(
    name='credit_model',
    source=credit_data,
    spec=model_spec,
    task=fdl.ModelTask.BINARY_CLASSIFICATION,
    input_type=fdl.ModelInputType.TABULAR
)

# Text-based model (NLP)
model = fdl.Model.from_data(
    name='sentiment_model',
    source=text_data,
    spec=model_spec,
    task=fdl.ModelTask.MULTICLASS_CLASSIFICATION,
    input_type=fdl.ModelInputType.TEXT
)

# Mixed data model (multimodal)
model = fdl.Model.from_data(
    name='multimodal_model',
    source=mixed_data,
    spec=model_spec,
    task=fdl.ModelTask.REGRESSION,
    input_type=fdl.ModelInputType.MIXED
)
Input type affects data validation, preprocessing, and available monitoring features. Choose the type that best matches your model’s primary input data format.

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TABULAR = ‘structured’

Structured tabular data with rows and columns. Used for traditional machine learning models that operate on structured datasets with well-defined features. This includes most supervised learning models trained on CSV data, database tables, or pandas DataFrames. Characteristics:
  • Fixed schema with defined columns
  • Numeric and categorical features
  • Traditional ML algorithms (trees, linear models, etc.)
  • Standard drift detection on individual features
Typical use cases:
  • Credit scoring models
  • Fraud detection systems
  • Customer churn prediction
  • Sales forecasting models
  • Risk assessment models
Supported data types: All DataType enum values

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TEXT = ‘text’

Natural language text data. Used for models that primarily process text inputs such as NLP models, language models, and text classification systems. Enables text-specific monitoring and embedding-based drift detection. Characteristics:
  • Text strings as primary input
  • Embedding-based feature monitoring
  • Text-specific preprocessing
  • Language model optimizations
Typical use cases:
  • Sentiment analysis models
  • Document classification
  • Named entity recognition
  • Text summarization models
  • Chatbots and conversational AI
Special considerations:
  • May require text embedding custom features
  • Supports text-specific enrichments
  • Drift detection on embedding vectors

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MIXED = ‘mixed’

Combination of structured and unstructured data. Used for multimodal models that process both structured tabular data and unstructured data like text, images, or embeddings. Enables comprehensive monitoring across different data types. Characteristics:
  • Multiple data modalities
  • Complex feature interactions
  • Flexible schema definitions
  • Advanced custom feature support
Typical use cases:
  • Recommendation systems (user features + content)
  • Fraud detection (transaction data + text descriptions)
  • Medical diagnosis (structured data + images/text)
  • E-commerce search (product features + text queries)
  • Content moderation (metadata + text/images)
Special considerations:
  • Requires careful schema design
  • May need multiple custom feature types
  • Complex drift monitoring setup