/AuditMissingValueReport
Audit for bias and leakage in a missing-value profile — e.g. a dataset with 12% missing income values.
Machine Learningadvanced#missing-data#imputation
Data Prep
60 commands
/AugmentTimeSeriesData
Generate synthetic augmentations for a time-series dataset — e.g. three years of daily store sales.
Machine Learningmedium#time-series#forecasting
/BalanceTabularDataset
Address class imbalance in a tabular training dataset — e.g. a churn dataset with 40 customer attributes.
Machine Learningeasy#tabular#csv
/EngineerOutlierSet
Engineer useful features from a set of detected outliers — e.g. transactions flagged as statistically unusual.
Machine Learningadvanced#outliers#anomalies
/CleanLabelSet
Clean and validate the training data in a set of ground-truth labels — e.g. labels applied by three different annotators with disagreements.
Machine Learningmedium#labels#annotations
/AuditTextCorpus
Audit for bias and leakage in a text corpus — e.g. 50,000 product reviews for sentiment training.
Machine Learningeasy#nlp#text
/BalanceMetadataSchema
Address class imbalance in a dataset metadata schema — e.g. a data card describing source.
Machine Learningadvanced#metadata#documentation
/SplitFeatureStore
Create a proper train/validation/test split of a feature store schema — e.g. customer features shared across five models.
Machine Learningmedium#feature-store#mlops
/EngineerImageDataset
Engineer useful features from an image dataset — e.g. 10,000 labeled defect photos from a factory line.
Machine Learningeasy#vision#images
/AuditSyntheticSample
Audit for bias and leakage in a synthetic data sample — e.g. generated variations of rare fraud cases.
Machine Learningadvanced#synthetic#generated
/AugmentMissingValueReport
Generate synthetic augmentations for a missing-value profile — e.g. a dataset with 12% missing income values.
Machine Learningmedium#missing-data#imputation
/BalanceTimeSeriesData
Address class imbalance in a time-series dataset — e.g. three years of daily store sales.
Machine Learningeasy#time-series#forecasting
/SplitTabularDataset
Create a proper train/validation/test split of a tabular training dataset — e.g. a churn dataset with 40 customer attributes.
Machine Learningadvanced#tabular#csv
/CleanOutlierSet
Clean and validate the training data in a set of detected outliers — e.g. transactions flagged as statistically unusual.
Machine Learningmedium#outliers#anomalies
/AuditLabelSet
Audit for bias and leakage in a set of ground-truth labels — e.g. labels applied by three different annotators with disagreements.
Machine Learningeasy#labels#annotations
/AugmentTextCorpus
Generate synthetic augmentations for a text corpus — e.g. 50,000 product reviews for sentiment training.
Machine Learningadvanced#nlp#text
/SplitMetadataSchema
Create a proper train/validation/test split of a dataset metadata schema — e.g. a data card describing source.
Machine Learningmedium#metadata#documentation
/EngineerFeatureStore
Engineer useful features from a feature store schema — e.g. customer features shared across five models.
Machine Learningeasy#feature-store#mlops
/CleanImageDataset
Clean and validate the training data in an image dataset — e.g. 10,000 labeled defect photos from a factory line.
Machine Learningadvanced#vision#images
/AugmentSyntheticSample
Generate synthetic augmentations for a synthetic data sample — e.g. generated variations of rare fraud cases.
Machine Learningmedium#synthetic#generated
/BalanceMissingValueReport
Address class imbalance in a missing-value profile — e.g. a dataset with 12% missing income values.
Machine Learningeasy#missing-data#imputation
/SplitTimeSeriesData
Create a proper train/validation/test split of a time-series dataset — e.g. three years of daily store sales.
Machine Learningadvanced#time-series#forecasting
/EngineerTabularDataset
Engineer useful features from a tabular training dataset — e.g. a churn dataset with 40 customer attributes.
Machine Learningmedium#tabular#csv
/AuditOutlierSet
Audit for bias and leakage in a set of detected outliers — e.g. transactions flagged as statistically unusual.
Machine Learningeasy#outliers#anomalies
/AugmentLabelSet
Generate synthetic augmentations for a set of ground-truth labels — e.g. labels applied by three different annotators with disagreements.
Machine Learningadvanced#labels#annotations
/BalanceTextCorpus
Address class imbalance in a text corpus — e.g. 50,000 product reviews for sentiment training.
Machine Learningmedium#nlp#text
/EngineerMetadataSchema
Engineer useful features from a dataset metadata schema — e.g. a data card describing source.
Machine Learningeasy#metadata#documentation
/CleanFeatureStore
Clean and validate the training data in a feature store schema — e.g. customer features shared across five models.
Machine Learningadvanced#feature-store#mlops
/AuditImageDataset
Audit for bias and leakage in an image dataset — e.g. 10,000 labeled defect photos from a factory line.
Machine Learningmedium#vision#images
/BalanceSyntheticSample
Address class imbalance in a synthetic data sample — e.g. generated variations of rare fraud cases.
Machine Learningeasy#synthetic#generated
/SplitMissingValueReport
Create a proper train/validation/test split of a missing-value profile — e.g. a dataset with 12% missing income values.
Machine Learningadvanced#missing-data#imputation
/EngineerTimeSeriesData
Engineer useful features from a time-series dataset — e.g. three years of daily store sales.
Machine Learningmedium#time-series#forecasting
/CleanTabularDataset
Clean and validate the training data in a tabular training dataset — e.g. a churn dataset with 40 customer attributes.
Machine Learningeasy#tabular#csv
/AugmentOutlierSet
Generate synthetic augmentations for a set of detected outliers — e.g. transactions flagged as statistically unusual.
Machine Learningadvanced#outliers#anomalies
/BalanceLabelSet
Address class imbalance in a set of ground-truth labels — e.g. labels applied by three different annotators with disagreements.
Machine Learningmedium#labels#annotations
/SplitTextCorpus
Create a proper train/validation/test split of a text corpus — e.g. 50,000 product reviews for sentiment training.
Machine Learningeasy#nlp#text
/CleanMetadataSchema
Clean and validate the training data in a dataset metadata schema — e.g. a data card describing source.
Machine Learningadvanced#metadata#documentation
/AuditFeatureStore
Audit for bias and leakage in a feature store schema — e.g. customer features shared across five models.
Machine Learningmedium#feature-store#mlops
/AugmentImageDataset
Generate synthetic augmentations for an image dataset — e.g. 10,000 labeled defect photos from a factory line.
Machine Learningeasy#vision#images
/SplitSyntheticSample
Create a proper train/validation/test split of a synthetic data sample — e.g. generated variations of rare fraud cases.
Machine Learningadvanced#synthetic#generated
/EngineerMissingValueReport
Engineer useful features from a missing-value profile — e.g. a dataset with 12% missing income values.
Machine Learningmedium#missing-data#imputation
/CleanTimeSeriesData
Clean and validate the training data in a time-series dataset — e.g. three years of daily store sales.
Machine Learningeasy#time-series#forecasting
/AuditTabularDataset
Audit for bias and leakage in a tabular training dataset — e.g. a churn dataset with 40 customer attributes.
Machine Learningadvanced#tabular#csv
/BalanceOutlierSet
Address class imbalance in a set of detected outliers — e.g. transactions flagged as statistically unusual.
Machine Learningmedium#outliers#anomalies
/SplitLabelSet
Create a proper train/validation/test split of a set of ground-truth labels — e.g. labels applied by three different annotators with disagreements.
Machine Learningeasy#labels#annotations
/EngineerTextCorpus
Engineer useful features from a text corpus — e.g. 50,000 product reviews for sentiment training.
Machine Learningadvanced#nlp#text
/AuditMetadataSchema
Audit for bias and leakage in a dataset metadata schema — e.g. a data card describing source.
Machine Learningmedium#metadata#documentation
/AugmentFeatureStore
Generate synthetic augmentations for a feature store schema — e.g. customer features shared across five models.
Machine Learningeasy#feature-store#mlops
/BalanceImageDataset
Address class imbalance in an image dataset — e.g. 10,000 labeled defect photos from a factory line.
Machine Learningadvanced#vision#images
/EngineerSyntheticSample
Engineer useful features from a synthetic data sample — e.g. generated variations of rare fraud cases.
Machine Learningmedium#synthetic#generated
/CleanMissingValueReport
Clean and validate the training data in a missing-value profile — e.g. a dataset with 12% missing income values.
Machine Learningeasy#missing-data#imputation
/AuditTimeSeriesData
Audit for bias and leakage in a time-series dataset — e.g. three years of daily store sales.
Machine Learningadvanced#time-series#forecasting
/AugmentTabularDataset
Generate synthetic augmentations for a tabular training dataset — e.g. a churn dataset with 40 customer attributes.
Machine Learningmedium#tabular#csv
/SplitOutlierSet
Create a proper train/validation/test split of a set of detected outliers — e.g. transactions flagged as statistically unusual.
Machine Learningeasy#outliers#anomalies
/EngineerLabelSet
Engineer useful features from a set of ground-truth labels — e.g. labels applied by three different annotators with disagreements.
Machine Learningadvanced#labels#annotations
/CleanTextCorpus
Clean and validate the training data in a text corpus — e.g. 50,000 product reviews for sentiment training.
Machine Learningmedium#nlp#text
/AugmentMetadataSchema
Generate synthetic augmentations for a dataset metadata schema — e.g. a data card describing source.
Machine Learningeasy#metadata#documentation
/BalanceFeatureStore
Address class imbalance in a feature store schema — e.g. customer features shared across five models.
Machine Learningadvanced#feature-store#mlops
/SplitImageDataset
Create a proper train/validation/test split of an image dataset — e.g. 10,000 labeled defect photos from a factory line.
Machine Learningmedium#vision#images
/CleanSyntheticSample
Clean and validate the training data in a synthetic data sample — e.g. generated variations of rare fraud cases.
Machine Learningeasy#synthetic#generated