ML Core Snap Pack

Overview

ML Core Snap Pack is part of the SnapLogic Data Science (Machine Learning) Snaps.

Use Snaps in this Snap Pack to perform core machine learning operations such as:

  • 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 machine learning libraries in Python.
  • Automate the process of exploring and tuning machine learning models for a given dataset within the resource limit.

Supported Version

This Snap Pack is tested against Python version 3.6.

Applicable only to the Remote Python Script Snap.


Snap Pack History

 Click to view/expand

4.27 (main12833)

  • No updates made.

4.26 (main11181)

  • No updates made.

4.25 (main9554)

  • No updates made.

4.24 (main8556)

  • No updates made.

4.23 (main7430)

  • No updates made.

4.22 (main6403)

  • No updates made.

4.21 (snapsmrc542)

  • No updates made.

4.20 Patch mlcore8770

  • Adds the log4j dependency to the ML Core Snaps to resolve the "Could not initialize class org.apache.log4j.LogManager" error. 

4.20 (snapsmrc535)

  • No updates made.

4.19 (snapsmrc528)

  • No updates made.

4.18 (snapsmrc523)

  • No updates made.

4.17 Patch ALL7402

  • Pushed automatic rebuild of the latest version of each Snap Pack to SnapLogic UAT and Elastic servers.

4.17 (snapsmrc515)

  • New Snap: Introducing the Clustering Snap that performs exploratory data analysis to find hidden patterns or groupings in data.
  • Enhanced the AutoML Snap. You can now:
    • Select algorithms to derive the top models.
    • Input the best model generated by another AutoML Snap from a previous execution.
    • View an interactive HTML report that contains statistics of up to 10 models.
  • Added the Snap Execution field to all Standard-mode Snaps. In some Snaps, this field replaces the existing Execute during preview check box.

4.16 (snapsmrc508)

  • New Snap: Introducing the AutoML Snap, which lets you automate the process of selecting machine learning algorithms and tuning hyperparameters. This Snap gives the best predictive model within the specified time limit.

4.15 (snapsmrc500)

  • New Snap Pack. Perform data modeling operations such as model training, cross-validation, and model-based predictions. Additionally, you can also execute Python scripts remotely. Snaps in this Snap Pack are: 
    • Cross Validator -- Classification
    • Cross Validator -- Regression
    • Predictor -- Classification
    • Predictor -- Regression
    • Remote Python Script
    • Trainer -- Classification
    • Trainer -- Regression
  • Releases the Remote Python Executor account and the Remote Python Executor Dynamic account for the Remote Python Script Snap.