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SAP-samples/hana-ml-py-codejam

SAP CodeJam material that walks through a first machine learning workflow in SAP HANA Cloud from Python, in Jupyter notebooks from HANA DataFrames to hyperparameter tuning and AutoML.

The exercise material for an SAP CodeJam on machine learning with SAP HANA Cloud and Python: a series of Jupyter notebooks run one at a time, from checking the setup and the basics of HANA DataFrames through exploratory data analysis, classification training with a first fit and then a train/test split, three preprocessing notebooks, hyperparameter tuning and AutoML. It follows the machine learning process as it develops through a Python client for SAP HANA, starting with setting up SAP Business Application Studio and a dev space. What a participant needs beyond the notebooks, hardware and software alike, is listed in the repository's separate prerequisites file rather than here.

GitHub24 ★

Facts

Last pushLicenceApache-2.0

Where it sits

Lives in
BTP/BAIP
Demonstrates
Predictive modelsBTPPredictive modelscapabilityTraining, deploying and calling a model that predicts a value from tabular business data: a classification, a regression, a forecast. Distinct from [LLM integration](capability:llm-integration), where a language model generates text, even where both run on the same service.
Requires
SAP HANA CloudSAP HANA CloudartifactThe database the notebooks train and score against, as the repository states the CodeJams are about.SAP Business Application StudioSAP Business Application StudioartifactWhere the notebooks are run: the setup exercise creates a dev space in it before the machine learning exercises start.

Who is behind it

SAP SamplesSAP SamplesOrganisationMaintains this
In the atlas: maintains 222@SAP-samplesmaintainerVitaliy RudnytskiyPersonContributes to this
In the atlas: contributes to 6, wrote 1@Sygyzmundovych1 contributor

What it declares

Every artifact

Type
Sample
Origin
By SAP
Use as
A reference

As a sample

Form
Workshop or CodeJam material

Connections3

README

quoted from SAP-samples/hana-ml-py-codejam under Apache-2.0, read
SAP-samples/hana-ml-py-codejam/README.mdOn GitHub

REUSE status

CodeJam - Getting Started with Machine Learning using SAP HANA and Python

Description

This repository contains the material for the CodeJam on "Getting Started with Machine Learning using SAP HANA and Python".

In this CodeJam you will learn how the Machine Learning process develops using a Python Client for SAP HANA.

Overview

Requirements

The requirements necessary to complete the exercises in this repository, including hardware and software specifications, are outlined in the prerequisites file.

Material organization

The material comprises a series of exercises, with each exercise being a Jupyter notebook to be executed.

Following the exercises

During the CodeJam, you will complete each exercise one at a time. Within each exercise, there are discussion points to be discussed upon with the entire CodeJam class, facilitated by the instructor and marker using the notation "🤓 Let's discuss".

If you complete any exercise ahead of others, kindly refrain from moving on to the next one. Instead, take the opportunity to delve deeper into what you've just accomplished and explore additional aspects of the covered subject matter. This approach ensures that we all progress together and can derive additional value through reflection via the questions (and answers).

The exercises

Here's an overview of the exercises in this CodeJam.

Make certain that you have successfully completed all the prerequisites

Setup:

Machine Learning:

  1. Check your setup
  2. (Optional) Basics of HANA DataFrames
  3. Exploratory Data Analysis, or EDA
  4. Training a ML model using Classification
  5. Training a ML model using Train/Test split
  6. Preprocessing - Exclude High Cardinality
  7. Preprocessing - Missing Values
  8. Preprocessing - Feature Engineering
  9. Model tuning
  10. Auto ML

Known Issues

Not known issues as of now.

Feedback

If you can spare a couple of minutes at the end of the session, please help me improve for next time by giving me some feedback.

Simply use this Give Feedback link to create a special "feedback" issue, and follow the instructions in there.

How to obtain support

Create an issue in this repository if you find a bug or have questions about the content.

For additional support, ask a question in SAP Community using SAP HANA tag.

Further connections and information

Here are a few pointers to resources for further connections and information:

Additional learning material

These recorded sessions are optional, but are available should you be interested to get an overview ahead of the event, or after the event to recap.

  1. 📺 Build your Machine Learning Scenario for your SAP HANA Cloud application from Python - Devtoberfest'22
  2. 📺 Accelerate your Machine Learning efforts: benefit from SAP HANA Cloud AutoML - SAP Community Call
  3. Fairness in Machine Learning: A New Feature in SAP HANA Cloud PAL 2023/Q4

Contributing

If you wish to contribute code, offer fixes or improvements, please send a pull request. Due to legal reasons, contributors will be asked to accept a DCO when they create the first pull request to this project. This happens in an automated fashion during the submission process. SAP uses the standard DCO text of the Linux Foundation.

License

Copyright (c) 2023-2024 SAP SE or an SAP affiliate company. All rights reserved. This project is licensed under the Apache Software License, version 2.0 except as noted otherwise in the LICENSE file.

Apache-2.0, read

Details

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first seen in the atlas
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Contributors1
Vitaliy Rudnytskiy

From the repository’s contributor list, .

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