The ML Core Snap Pack delivers a comprehensive collection of Snaps that handle machine learning lifecycle—from model training and validation to prediction and optimization. Whether you’re a data scientist building sophisticated predictive models or a business analyst automating forecasts and classifications, this Snap Pack provides production-ready implementations of state-of-the-art machine learning algorithms, all accessible through the SnapLogic visual interface.
Train models on your data, validate their accuracy with K-fold cross-validation, apply them to predict outcomes on new data, and systematically explore different algorithms and parameters to find the optimal solution for your use case. You get full flexibility of the Python rich ecosystem of machine learning libraries—scikit-learn, pandas, NumPy, and more—the ML Core Snap Pack lets you execute custom Python scripts directly within your workflows, giving you the best of both worlds: the speed and accessibility of low-code integration combined with the unlimited power of code when you need it.
Use Snaps in this Snap Pack to:
- Perform K-fold cross validation for classification and regression datasets.
- Train models using state-of-the-art machine learning algorithms.
- Apply models to predict unlabeled data.
- Execute Python scripts and take advantage of Python machine learning libraries
- Automate the process of exploring and tuning machine learning models
The ML Core Snap Pack includes the following Snaps:
- Cross-Validator – Classification: Cross-validate classification data models using algorithms and choose the best possible algorithm for the dataset.
- Cross-Validator – Regression: Cross-validate regression data models using algorithms and choose the best possible algorithm for the dataset.
- Predictor – Classification: Predict the unlabeled data in a classification dataset using a data model.
- Predictor – Regression: Predict the unlabeled data in a regression dataset using a data model.
- Trainer – Classification: Train/generate a data model for a classification dataset using algorithms.
- Trainer – Regression: Train/generate a data model for a regression dataset using algorithms.
- Remote Python Script: Execute Pythons scripts remotely on the Python server.
- AutoML: Automate the process of training a large selection of candidate machine learning models by providing minimal inputs.
- Clustering: Perform exploratory analysis by identifying hidden groupings in data
To learn more, please check out the documentation page.
