Overview

You must configure ML Core accounts to enable ML Core Snaps to connect to ML Core instances.

See Accounts to learn more about SnapLogic Accounts.

Snap Compatibility

Snap	Remote Python Executor Account	Remote Python Executor Dynamic Account
Remote Python Script	√	√
AutoML	x	x
Clustering	x	x
Cross Validator (Classification)	x	x
Cross Validator (Regression)	x	x
Predictor (Classification)	x	x
Predictor (Regression)	x	x
Trainer (Classification)	x	x
Trainer (Regression)	x	x

Configuring ML Core Accounts

You can configure ML Core accounts in SnapLogic using either the Designer or Manager.

Using SnapLogic Designer

Log in to Snaplogic to open the Designer.
Drag an ML Core Snap to the canvas.
Click the Snap to open the Snap's settings.
Click the Account tab.
Select the account that you want to use from the Account Reference drop-down list, or click Add Account to create a new account.

The account types that are compatible with the selected Snap appear under Account Type. For details about Snaps and compatible Account types, see Snap Compatibility.
Select the location where you want to create the account, and then select the account type.
Click Continue to open the account Settings.
Enter the required account details. For detailed guidance on how to provide information associated with each account type, use the following links:
Optionally, enter additional information on this account in the Notes field of the Info tab.
Click Validate to verify the account information, if applicable.
Click Apply to complete configuring the account.

Using SnapLogic Manager

Log in to Snaplogic. Click the Manager tab.
Navigate to the project with which you want to associate the new account. Click Create > Account > ML Core, followed by the appropriate account type.

The Create Account page associated with the selected account type appears.
In Settings, enter the required account details. For detailed guidance on how to provide information associated with each account type, use the following links:
Optionally, enter additional information about this account in the Notes field of the Info tab.
Click Validate to verify the account information, if applicable.
Click Apply to complete configuring the ML Core account.

Avoid updating the account credentials while Pipelines using this account are being executed. This may lead to unexpected results, including locking your ML Core account.

Snap Pack History

Click to view/expand

Release	Snap Pack Version	Date	Type	Updates
February 2024	main25112	14 Feb 2024	Stable	Updated and certified against the current SnapLogic Platform release.
November 2023	main23721	Nov 8, 2023	Stable	Updated 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 2023	433patches21854	14 Jul 2023	Latest	Fixed an issue with the Cross Validator (Classification) Snap where the native Windows DLL caused the Snaplex to stall
May 2023	433patches21644	28 Jun 2023	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	10 May 2023	Stable	Upgraded with the latest SnapLogic Platform release.
February 2023	main19844	09 Feb 2023	Stable	Upgraded with the latest SnapLogic Platform release.
November 2022	main18944	10 Nov 2022	Stable	Upgraded with the latest SnapLogic Platform release.
August 2022	main17386	11 Aug 2022	Stable	Upgraded with the latest SnapLogic Platform release.
4.29	429patches16809	20 Jul 2022	Latest	Removed the log4j dependency from the ML Core Snaps due to security vulnerabilities.
4.29	main15993	14 May 2022	Stable	Upgraded with the latest SnapLogic Platform release.
4.28	main14627	20 Jul 2022	Stable	Upgraded with the latest SnapLogic Platform release.
4.27	427patches13948	07 Jan 2022	Latest	Fixed an issue with the following Snaps, where a deadlock occurred when data is loaded from both the input views. Predictor Classification Predictor Regression Clustering
4.27	main12833	13 Nov 2021	Stable	Upgraded with the latest SnapLogic Platform release.
4.26	main11181	14 Aug 2021	Stable	Upgraded with the latest SnapLogic Platform release.
4.25	main9554	08 May 2021	Stable	Upgraded with the latest SnapLogic Platform release.
4.24	main8556	13 Feb 2021	Stable	Upgraded with the latest SnapLogic Platform release.
4.23	main7430	14 Nov 2020	Stable	Upgraded with the latest SnapLogic Platform release.
4.22	main6403	12 Sep 2020	Stable	Upgraded with the latest SnapLogic Platform release.
4.21	snapsmrc542	09 May 2020	Stable	Upgraded with the latest SnapLogic Platform release.
4.20 Patch	mlcore8770	18 Mar 2020	Stable	Adds the log4j dependency to the ML Core Snaps to resolve the "`Could not initialize class org.apache.log4j.LogManage`r" error.
4.20	snapsmrc535	08 Feb 2020	Stable	Upgraded with the latest SnapLogic Platform release.
4.19	snapsmrc528	14 Nov 2019	Stable	Upgraded with the latest SnapLogic Platform release.
4.18	snapsmrc523	10 Aug 2019	Stable	Upgraded with the latest SnapLogic Platform release.
4.17 Patch	ALL7402	11 Jun 2019	Latest	Pushed automatic rebuild of the latest version of each Snap Pack to SnapLogic UAT and Elastic servers.
4.17	snapsmrc515	11 Jun 2019	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	16 Feb 2019	Stable	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	15 Dec 2018	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.