Load a dataset from various sources: uploaded files (full path), data directory (filename), URLs, or sklearn datasets

Validate dataset quality and check for issues

Generate comprehensive data profile and statistics

Apply preprocessing transformations to dataset

Clean dataset by handling missing values and outliers

Split dataset into train/validation/test sets

List all loaded datasets with their metadata

Get detailed information about a specific dataset

Compare structure and statistics of two datasets

Apply the same operation to multiple datasets

Create a sample from a dataset

Export dataset to file

Remove a dataset from memory and optionally delete files

Clear all datasets and cached data from current session

Train a machine learning model with configurable persistence (memory-only, filesystem, or hybrid storage)

Evaluate a single trained model with comprehensive metrics and cross-validation

Compare multiple trained models on the same dataset with statistical significance testing

Perform comprehensive hyperparameter tuning for a model with various search strategies

Get detailed information about a trained model including metadata and performance

List all available machine learning algorithms with descriptions

Create a new experiment for organizing runs

Start a new run within an experiment

Log parameters to the current run

Log metrics to the current run

Log an artifact (file) to the current run

End the current run

List all experiments

Get details of a specific experiment

List runs from an experiment

Compare multiple runs