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In this article

Table of Contents

Overview

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

Use Snaps in this Snap Pack are useful in performing 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.

Note

Applicable only to the Remote Python Script Snap.



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Excerpt

Snap Pack History

4.23 (main7430)

  • No updates made.

4.22 (main6403)

  • No updates made.

4.21 (snapsmrc542)

  • No updates made.

4.20 Patch mlcore8770

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Release Snap Pack VersionDateType  Updates
November 2024main29029 StableUpdated and certified against the current SnapLogic Platform release.

August 2024

main27765

 

Stable

Upgraded the org.json.json library from v20090211 to v20240303, which is fully backward compatible.

May 2024main26341 StableUpdated and certified against the current SnapLogic Platform release.
February 2024main25112 StableUpdated and certified against the current SnapLogic Platform release.
November 2023main23721Nov 8, 2023StableUpdated and certified against the current SnapLogic Platform release.

August 2023

main22460

Aug 16, 2023

Stable

Updated and certified against the current SnapLogic Platform release.

May 2023433patches21854 Latest

Fixed an issue with the Cross Validator (Classification) Snap where the native Windows DLL caused the Snaplex to stall

May 2023433patches21644 Latest

Improved an error message in the Remote Python Script Snap to explain the reason and resolution for the case where a Python script has errors.

May 2023

main21015 

Stable

Upgraded with the latest SnapLogic Platform release.

February 2023main19844 StableUpgraded with the latest SnapLogic Platform release.
November 2022main18944 Stable

Upgraded with the latest SnapLogic Platform release.

August 2022 main17386 Stable

Upgraded with the latest SnapLogic Platform release.

4.29429patches16809 Latest

Removed the log4j dependency from the ML Core Snaps due to security vulnerabilities.

4.29main15993 StableUpgraded with the latest SnapLogic Platform release.
4.28main14627 Stable

Upgraded with the latest SnapLogic Platform release.

4.27427patches13948 Latest

Fixed an issue with the following Snaps, where a deadlock occurred when data is loaded from both the input views.

4.27main12833 StableUpgraded with the latest SnapLogic Platform release.
4.26main11181 StableUpgraded with the latest SnapLogic Platform release.
4.25main9554 StableUpgraded with the latest SnapLogic Platform release.
4.24main8556 StableUpgraded with the latest SnapLogic Platform release.
4.23main7430 StableUpgraded with the latest SnapLogic Platform release.
4.22main6403 StableUpgraded with the latest SnapLogic Platform release.
4.21snapsmrc542 StableUpgraded with the latest SnapLogic Platform release.
4.20 Patchmlcore8770 Stable

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
snapsmrc535 StableUpgraded with the latest SnapLogic Platform release.
4.19
(snapsmrc528)No updates made
snapsmrc528 StableUpgraded with the latest SnapLogic Platform release.
4.18
(snapsmrc523)No updates made
snapsmrc523 StableUpgraded with the latest SnapLogic Platform release.
4.
17 Patch 
17 PatchALL7402 Latest

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

4.17
(snapsmrc515)
snapsmrc515
 
Latest
  • 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)
snapsmrc508
 
StableNew 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)
15snapsmrc500
 
Stable
  • 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.