Machine Learning Guide
PUBLIC
SAP Data Intelligence
2024-04-20
? 2024 SAP SE or an SAP affiliate company. All rights reserved.
Machine Learning Guide
THE BEST RUN
Content
1
Machine Learning in SAP Data Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3
2
ML Scenario Manager. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1
Setting Up Your Machine Learning Scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Registering a Dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Adding a Jupyter Notebook. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Adding a Pipeline. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8
2.2
Scenario Versions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Creating a New Version of Your Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3
Exporting and Importing Scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4
Metrics Explorer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Browsing Run Collections and Runs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Comparing Runs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.5
Troubleshooting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Workspace Contains Files Larger Than 2 MB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3
JupyterLab Environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.1
Running Kernels and Terminals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2
Setting Up a Virtual Environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3
Accessing DI_DATA_LAKE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4
Using the Data Browser Extension. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.5
Copying Resources to MLSM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4
Python SDK. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1
Setting the Context. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29
4.2
Retrieving ML Scenario Metadata. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29
4.3
Working with Pipelines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.4
Versioning an ML Scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.5
Executing or Deploying Pipelines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.6
Working with Training Pipelines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31
4.7
Accessing Artifacts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.8
Tracking Metrics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Runs and Run Collections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Using the SDK to Track Metrics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5
2
Deprecation of Machine Learning Components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
PUBLIC
Machine Learning Guide
Content
1
Machine Learning in SAP Data
Intelligence
This guide provides an overview of key concepts related to machine learning and demonstrates how SAP Data
Intelligence can be used to perform data science tasks.
SAP Data Intelligence empowers scientists to leverage the power of machine learning and gain valuable
insights from their data. With the ML Scenario Manager, users can model data, while the Metrics Explorer
provides tools for visualizing and analyzing data.
If you prefer to access the machine learning functionality programmatically, you can do so using the software
development kit (SDK), which is available from a Jupyter notebook or from the Pipeline operator.
Data Modeling
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ML Scenario Manager [page 5]
Machine Learning Guide
Machine Learning in SAP Data Intelligence
PUBLIC
3
Data Visualization
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Metrics Explorer [page 16]
Software Development Kit
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4
Python SDK [page 28]
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Machine Learning Guide
Machine Learning in SAP Data Intelligence
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ML Scenario Manager
ML Scenario Manager helps you to organize your data science artifacts and to manage your tasks in a central
location. As a multi-faceted data science application, it is built around the key concept of the machine learning
(ML) scenario, which may contain datasets, pipelines, and Jupyter notebooks.
You access ML Scenario Manager from the SAP Data Intelligence launchpad. You can then use it to create
your machine learning scenarios and add artifacts. You can also manage your model performance metrics and
deployment history. If necessary, you can version an ML scenario as part of your end-to-end workflow and
create a new branch from a previous version.
A typical process within ML Scenario Manager involves the following:
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Managing your datasets and model artifacts
Creating Jupyter notebooks for your experiments (see JupyterLab Environment [page 22])
Creating and managing data pipelines
Viewing executions and performance metrics
Tracking your model deployments
See ML Scenario Manager in Action
If you'd like to see ML Scenario Manager in action, check out the video in the Web version of this document.
Related Information
Setting Up Your Machine Learning Scenario [page 6]
Scenario Versions [page 12]
Exporting and Importing Scenarios [page 13]
Metrics Explorer [page 16]
Machine Learning Guide
ML Scenario Manager
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