The ibm-watson-machine-learning library¶
The ibm-watson-machine-learning Python library allows you to work with IBM Watson Machine Learning services.
You can train, store, and deploy your models, score them using APIs, and finally integrate them with your application
development.
For supported product offerings refer to Product Offerings section.
- Installation
- Setup
- API
- Prerequisites
- Modules
- Core
- Connections
- Data assets
- Deployments
- Export/Import
- Factsheets (IBM Cloud only)
- Hardware specifications
- Helpers
- Model definitions
- Package extensions
- Repository
- Script
- Service instance
- Set
- Shiny (IBM Cloud Pak for Data only)
- Software specifications
- Spaces
- Training
- Enums
ClassificationAlgorithmsClassificationAlgorithmsCP4DDataConnectionTypesDirectionsForecastingAlgorithmsForecastingAlgorithmsCP4DForecastingPipelineTypesImputationStrategyMetricsMetricsToDirectionsPipelineTypesPositiveLabelClassPredictionTypeRegressionAlgorithmsRegressionAlgorithmsCP4DRunStateTypesSamplingTypesTShirtSizeTimeseriesAnomalyPredictionAlgorithmsTimeseriesAnomalyPredictionPipelineTypesTransformersVisualizationTypes
- Federated Learning
- AutoAI
- Working with AutoAI class and optimizer
- Configure optimizer with one data source
- Configure optimizer with joined data (IBM Cloud Pak for Data only)
- Configure optimizer for time series forecasting (IBM Cloud only)
- Configure optimizer for time series forecasting with supporting features (IBM Cloud only)
- Get configuration parameters
- Fit optimizer
- Get run status, get run details
- Get data connections
- Get preprocessed data connection (joined data only)
- Summary
- Get pipeline details
- Get pipeline
- Working with deployments
- Web Service
- Batch
- Working with DataConnection
- AutoAI experiment
- DataConnection Modules
- Deployment Modules for AutoAI models
- Working with AutoAI class and optimizer
- Core
- Samples
- Changelog