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Build your Machine Learning Scenario for your SAP HANA Cloud application from Python

Build your Machine Learning Scenario for your SAP HANA Cloud application from Python

https://groups.community.sap.com/t5/devtoberfest/build-your-machine-learning-scenario-for-your-sap-hana-cloud/ec-p/9071#M45

Learn about how the Python Machine Learning client for SAP HANA can be used to build classification, regression, or time series forecasting scenarios using the Predictive Analysis Library (PAL) or the Automated Predictive Library (APL).

See how the latest Machine Learning AutoML capabilities can help to build even better models in less time.

Vitaliy Rudnytskiy

October 05, 2022
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  1. Public
    Build your Machine Learning Scenario for your
    SAP HANA Cloud application from Python
    October 5th, 2022
    Christoph Morgen, SAP SE

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  2. 2
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    The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of SAP.
    Except for your obligation to protect confidential information, this presentation is not subject to your license agreement or any other service
    or subscription agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or any related
    document, or to develop or release any functionality mentioned therein.
    This presentation, or any related document and SAP's strategy and possible future developments, products and or platforms directions and
    functionality are all subject to change and may be changed by SAP at any time for any reason without notice. The information in this
    presentation is not a commitment, promise or legal obligation to deliver any material, code or functionality. This presentation is provided
    without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a
    particular purpose, or non-infringement. This presentation is for informational purposes and may not be incorporated into a contract. SAP
    assumes no responsibility for errors or omissions in this presentation, except if such damages were caused by SAP’s intentional or gross
    negligence.
    All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from
    expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates,
    and they should not be relied upon in making purchasing decisions.
    Disclaimer

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    Please watch the recording and leave your questions at:
    https://groups.community.sap.com/t5/devtoberfest/build-your-
    machine-learning-scenario-for-your-sap-hana-cloud/ec-p/9071#M45

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    • SAP HANA Machine Learning
    ‒ Machine Learning function libraries
    ‒ Machine Learning clients for Data Scientists
    ‒ Demo
    • Data Science to Development Handshake
    ‒ ML Python to SQL Code Generation
    ‒ Demo
    • Summary and Outlook
    • Q&A
    Agenda

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    Augmenting Applications with SAP HANA Machine Learning
    Embedding AI into Applications running on SAP HANA
    § Leverage native Machine Learning out of the box
    – Automated and trending expert machine learning algorithms
    for embedded use and processing with in-memory performance
    – Native interfaces for Data Scientists in R and Python
    – Further enrich with document store, spatial and graph processing,
    search and text processing capabilities
    § Get SAP HANA Cloud advantages on top
    – Real-time federation, data lake and scalability
    § Build comprehensive multi-model SAP Applications
    – Available in all application development scenarios
    Documentation SAP HANA Machine Learning Overview
    Advanced analytical processing
    Graph
    Machine
    Learning Search
    Spatial Doc Store
    Predictive Analysis
    Library
    Automated Predictive
    Library
    Python / R
    machine learning client
    SQL database
    and developer client
    SAP HANA Cloud
    SAP Application specialized function libraries
    Text
    processing

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    Classic Machine Learning Scenarios
    Predicting customer behavior like churn, fraud or
    buying behavior (classification)
    Predicting car prices, based on model
    characteristics and market trends (regression)
    Enabling marketers to develop targeted
    marketing programs by grouping customers
    (clustering)
    Provide personalized product
    recommendations by analyzing product
    associations, individual purchase history and
    external factors (recommender system)
    SAP HANA Cloud | Embedded Machine Learning
    Typical Scenarios Addressed
    Forecasting future sales, demand, cost, etc.
    based on historic time related data
    (time series forecasting)
    Analyzing shopping baskets to suggest product
    placements or additional purchases to a customer
    (association analysis)
    Detecting anomalies in financial transactions
    for fraud analysis, or in machine sensor data
    for predictive maintenance (outlier detection)
    In a given social network, you seek to infer which
    new interactions among its members are likely to
    occur in the near future (link analysis / prediction)

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    Approach | Develop a Machine Learning Application
    Understand business
    problem and build
    machine learning models
    in Jupyter Notebook
    Generate design-time
    artefacts for machine
    learning scenario via
    hana-ml library
    Import design-time
    artefacts into CAP project
    and configure database
    module
    Consume machine
    learning models and
    integrate prediction into
    business app
    Build Develop Consume
    Generate
    Data Scientist App Developer

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    SAP HANA Cloud | Automated Predictive Library (APL)
    Native In-Database Automated Predictive Analytics
    Automated Analytics Engine in SAP HANA Cloud
    § Automated Predictive Library (APL)
    • Addresses ML scenarios like Classification, Regression
    or Time Series Forecasting (and more)
    • Automated analysis covers steps from variable selection,
    data preparation, variable encoding, missing value
    handling, outlier handling, binning and banding, model
    testing and best model selection
    § Automation is the key to broad and fast adoption
    • The APL provides simple SQL procedures for developers
    to create, train, apply, deploy predictive models
    • Quick and easy to leverage for non-expert Data Scientists
    • Python machine learning client support for APL
    • High productivity and fast time to value
    Automated Predictive Library (APL)
    Classification
    Regression
    Cluster
    analysis
    Time series
    forecasting
    Association
    analysis
    Recommendation
    Link analysis
    SAP HANA Cloud
    Learn how to get started with APL and SAP HANA Cloud and documentation.

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    § Benefits
    • For easy consumption and fast adoption, SAP HANA Cloud APL provides simple procedure
    functions, sample data sets and sample SQL scripts to application developers
    • All results in the training and prediction phase can be queried for visualization and explanation
    Using Automated Predictive Library (APL) in SAP HANA Cloud
    Data Science made easy for Application Developers
    Prepare Data Build and Train Models Deploy and Predict
    Machine Learning Tasks
    Automated by APL
    Call CREATE_MODEL_AND_TRAIN () Call APPLY_MODEL ()

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    SAP HANA Cloud | Predictive Analysis Library (PAL)
    Native In-Database Machine Learning
    § SAP HANA Cloud embeds multiple machine learning
    libraries, designed and optimized for massive parallel in-
    memory processing
    § Predictive Analysis Library (PAL)
    • Addresses ML scenarios like Classification, Regression or Time
    Series Forecasting (and more)
    • Expert algorithm library, with over 100 classic and trending
    machine learning algorithms
    • Algorithm-/model pipeline support, AutoML for classification /
    regression / time series (best pipeline and model parameters)
    • Segmented modeling, like segmented / massive Forecasting
    • Parallel and real-time transaction performance inference
    • Explainability for interpretability of model predictions
    § Easy to develop and simple to embed with applications
    • Simple SQL interface and Python / R machine learning client
    • Supports both expert data scientists and developer personas
    Predictive Analysis Library (PAL)
    Classification
    Regression
    Cluster
    analysis
    Time series
    forecasting
    Association
    analysis
    Recommender
    System
    Link prediction
    Outlier detection
    SAP HANA Cloud
    Learn how to get started with PAL and SAP HANA Cloud, see PAL documentation.

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    SAP HANA Cloud | Predictive Analysis Library (PAL)
    Algorithm overview by category
    Classification Analysis
    § Decision Tree Analysis (CART, C4.5, CHAID)
    , Logistic Regression, Support Vector
    Machine, K-Nearest Neighbor, Naïve Bayes,
    Confusion Matrix, AUC,
    Online multi-class Logistic Regression*
    § Multilayer Perception (back propagation
    Neural Network)
    § Random Decision Trees, Hybrid Gradient
    Boosting Tree (HGBT)#,, Continuous HGBT*
    § Unified Classification# incl. explainability,
    segmented (massive) classification
    Regression
    § Multiple Linear Regression,
    Online Linear Regression*
    § Polynomial-, Exponential-, Bi-Variate
    Geometric-, Bi-Variate Natural Logarithmic-
    Regression
    § Generalized Linear Model (GLM)
    § Cox Proportional Hazards Model
    § Random Decision Trees, Hybrid Gradient
    Boosting Tree (HGBT) #, Continuous HGBT*
    § Unified Regression* incl. explainability,
    segmented (massive) regression
    Pipeline and AutoML
    § Pipeline-models, -fit and -predict
    § AutoML incl. data preprocessing, classi-
    fication, regression, time series forecasting
    Association Analysis
    § Apriori, Apriori Lite, FP-Growth
    § K-Optimal Rule Discovery (KORD)
    Discovery, Sequential Pattern Mining
    Link Prediction
    § Link Prediction (Common Neighbors,
    Jaccard’s Coefficient, Adamic/Adar, Katzβ),
    PageRank
    Recommender Systems
    § Factorized Polynomial Regression Models,
    Alternating least squares, Field-aware
    Factorization Machines (FFM)
    Text Processing
    § Conditional Random Field, Latent Dirichlet
    Allocation
    § TF-IDF*, term analysis*, text
    classification*, get related terms /
    documents*, get relevant terms /
    documents*, get suggested terms*
    Data Preprocessing
    § Sampling, Partitioning, SMOTE, TomekLink,
    SMOTETomek#
    § Binning / Discretize, Missing Value Handling,
    Scaling, Feature Selection*
    § Isolation Forest*
    Statistical & Multivariate Analysis
    § Univariate Analysis (Data Summary, Mean,
    Median, Variance, Stand. Deviation, Kurtosis,
    Skewness, ..)
    § Kernel Density Estimation, Entropy
    § Correlation Function (with confidence)
    § Multivariate Analysis (Covariance Matrix,
    Pearson Correlations Matrix),
    Condition Index
    § Principal Component Analysis (PCA)/PCA
    Projection, TSNE, Categorial PCA
    § Linear Discriminant Analysis
    § Multidimensional scaling,
    Factor Analysis
    § Chi-squared Tests: Quality of Fit,
    Test of Independence, ANOVA, F-test (equal
    variance test)
    § One-sample Median Test, T Test, Wilcox Signed
    Rank Test, Kolmogorov-Smirnov Test*
    § Inter-Quartile Range, Variance Test, Grubbs
    Outlier Test , Anomaly Detection (KMeans)
    § Random Distribution Sampling, Markov Chain
    Monte Carlo (MCMC)#
    § Distribution Fitting, Cumulative Distribution
    Function, Distribution Quantile
    Misc. Functions
    § Kaplan-Meier Survival Analysis, Weighted
    Scores Table, ABC Analysis, Tree model
    visualization#
    Cluster Analysis
    § K-Means, Accelerated K-Means, K-Medoids, K-
    Medians, Geo- / DBSCAN, Agglomerate
    Hierarchical Clustering*, Slight Silhouette,
    Cluster Assignment
    § Kohonen Self-Organizing Maps, Affinity
    Propagation, Gaussian Mixture Model
    § segmented (massive) Unified Clustering#,
    Spectral clustering*
    Time Series Analysis
    § Single-, Double-, Triple-, Brown-, Auto
    Exponential Smoothing, Unified Exponential
    Smoothing (incl. massive segmentation)*
    § Auto-ARIMA, Online ARIMA*,
    Vector-ARIMA*, ARIMA_EXPLAIN*
    § Additive Model Analysis#, GARCH*, BSTS*
    § Croston, Croston TSB*, Linear Regression
    with damped trend and seasonal adjust,
    Intermittent Time Series Forecast*
    § Fast Dynamic Time Warping# , DTW*,
    Hierarchical Forecasting
    § FFT, Discrete Wavelet/ Wavelet Packet
    Transform*, Periodogram*
    § White Noise-, Trend-, Stationary-*, Seasonality-
    Test, Change Point Detection, Bayesian
    Change Point Detection*, Outlier Detection*,
    TS Imputation*, Forecast Accuracy Measures
    § LSTM*, Attention*, LTSF*
    § Segmented (massive) Forecasting*
    SAP HANA Predictive Analysis Library documentation
    #SAP HANA 2 SPS05 & HANA Cloud | *SAP HANA 2 SPS06 & HANA Cloud | *New in SAP HANA Cloud | As of SAP HANA Cloud 2022 Q3 (CE2022.30))

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    SAP HANA Cloud | Using Predictive Analysis Library (PAL)
    In-database machine learning made easy using SQL
    SAP Applications embedding SAP HANA Cloud ML
    § Simple developer SQL interface to Predictive Analysis Library / AFLs
    – SAP HANA Cloud Database Explorer with simple SQL call interface to
    PAL / AFL procedures in _SYS_AFL schema
    § Application Development and Application Integration
    – ABAP application integration via ABAP Managed Database Procedures,
    standardized in S/4HANA with Intelligent Scenario Lifecycle Management
    – Business Application Studio allows SAP HANA ML/AFL procedure embedding within
    HDI design-time database procedures of BTP Cloud Application projects
    – Fiori-/CAP-applications allow consumption of SAP HANA ML/AFL procedures via
    advanced SAP HANA native objects like table functions of HDI projects

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    Leveraging SAP HANA’s data science capabilities
    § Allow scripting in Python or R, while instructing remote
    processing of data and advanced analytics in SAP HANA Cloud
    § Use the HANA dataframe object as virtual data reference for
    data preprocessing, transformation and analysis, including
    exploratory data analysis (EDA) visualizations
    § Leverage the Predictive Analyis Library (PAL) in Python / R,
    allowing the expert Data Scientists a simple conversion from
    standard Python-packages to HANA embedded ML models and
    their operationalization
    § Automated Predictive Library (APL) functions exposing SAP
    HANA‘s AutoML and non-expert predictive functions in Python
    § Model storage and ML model performance reports
    § Leverage SAP HANA Spatial and Graph capabilities in Python
    SAP HANA Cloud | Python/R Machine Learning Clients
    Data Scientist using R or Python
    Python / R machine learning client
    Learn how to get started with PAL and SAP HANA Cloud, APL and SAP HANA Cloud see Python samples.
    Python machine learning client documentation here
    R machine learning client documentation here

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    The HANA dataframe
    § This module represents a database query as a dataframe in Python.
    Operations are designed to not bring data back from the database, unless explicitly collected.
    § Dataframe creation
    • Using a ConnectionContext against a table or using a SQL query
    • Has attributes Name and Columns
    § Methods against a dataframe
    • Explore your data set using
    a simple describe call
    SAP HANA Cloud | Python Machine Learning Client – The Dataframe
    • various methods available: Agg, bin, corr, count (replace nrows), empty, hasna, is_numeric, rename_columns
    (multi column rename), union, privot, save as view, join with select, …

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    Exploratory data analysis visualizations
    § This module’s functions are designed to delegate the data analysis logic into SAP HANA Cloud,
    and only provide back the result set required for visual display
    • Bar- and pie-plots, distribution plots, box-whisker plots, scatter heat-map like plots, correlation plots, …
    include capabilities like implicit binning of column values
    SAP HANA Cloud | Python Machine Learning Client – Data Exploration

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    SAP HANA Cloud | Python Machine Learning Client – Classification Example
    Classification scenario example
    § Leveraging PAL unified classification procedure,
    ‒ single interface for Decision trees, Hybrid gradient boosting tree,
    Logistic regression, Multi-class logistic regression, Naïve Bayes,
    Random decision trees, Support Vector Machine
    ‒ Includes optimal model parameter selection, fit / score / predict
    functions, additional debrief statistics and metric data for model
    value plot, …
    Fit model
    Predict incl. explainability
    Score and evaluate model
    For examples see /SAP-samples/hana-ml-sample/Python.

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    SAP HANA Cloud | Embedded ML – Value Add
    Unique benefits and differentiation of SAP HANA Cloud embedded ML
    § State of the art algorithms for classic ML scenarios
    • Classification, regression, forecasting, clustering, …
    • Automated ML functions (APL) as well as expert ML functions (PAL)
    incl. trending functions like Random Decision Trees, Gradient Boosting, …
    § Executes co-located with data and database transaction
    • Benefits from HANA in-memory processing and performance
    • Supports scenarios like massive, parallel segmented forecasting
    • Fastest ML inference within transaction processing
    § Simple architecture
    • No-extra service or machine required
    • Apply in multi-model context, in combination with spatial, graph, text analytics
    § Multi-role and user interface
    • SQL for database developers
    • Python / R machine learning client for Data Scientist
    • Integrate into SAP Applications via ABAP/HANA-SQL capabilities

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    • Generate HDI design-time project files
    ‒ Based on „final“ fit / predict python calls
    ‒ Generated SQL is captured and merged
    into a HANA ML HDI project template
    • Incl. HDI-required synonyms, role grants etc.
    ‒ Share as GIT-repository
    Ø Simple hand-over of ML scenario artefacts
    to BAS developer
    Build ML scenarios by Data Scientists in Python
    • Leveraging the Python ML client for HANA
    • Generate PAL SQL code
    ‒ Python methods for all PAL functions
    Data Science to Development Handshake│ML Python to SQL Code Generation

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    Build ML scenarios by Data Scientists in Python
    • Leveraging the Python ML client for HANA
    • Generate HDI design-time project files
    ‒ Based on „final“ fit / predict python calls
    • Enable SQL trace
    • Execute ML scenario fit / predict
    ‒ Generated SQL is captured and merged
    into a HANA ML HDI project template
    • Use artifact.HanaGenerator method
    • HDI artifacts
    - base procedure (algorithm configuration)
    - consumption procedure (input data binding /
    application integration)
    + Synonyms for PAL procedures*,
    role grants* and user provided service-reference
    for _SYS_AFL schema access*
    Data Science to Development Handshake│ML Python to SQL Code Generation
    *required to embed HANA ML in HDI-XSA/BAS applications

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    Build ML scenarios by Data Scientists in Python
    • Generated HDI design-time project files
    ‒ Filesystem / git-repository artifacts
    Data Science to Development Handshake│ML Python to SQL Code Generation
    Business Application Studio
    • Import / clone project from GIT
    • Build MTA project
    • Deploy MTA archive
    User provided service-reference*
    Ÿ * UPS user requires privilege roles to grant use of _SYS_AFL functions like
    AFL__SYS_AFL_AFLPAL_EXECUTE_WITH_GRANT_OPTION

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    Reference Blog | Prediction of Fuel Prices in Germany
    Please check our blog post: SAP Data & Analytics Showcase – Develop a Machine Learning Application on SAP Business Technology Platform
    and SAP BTP Data & Analytics Showcase – Overall Integration Demo

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    App Development Flow│Configure and Develop CAP Application in BAS
    *HDI A and HDI B are belonging to the same HANA Cloud instance
    HDI - A HDI - B
    Training data
    for ML
    CAP
    application
    SAP Business Technology Platform
    SAP HANA Cloud
    User Provided Service!
    Develop Consume
    Build Generate
    Business Application Studio
    Design-time
    database artefacts
    • Synonyms
    • Roles / Grants
    • Procedures
    • Table Structures
    Jupyter Notebook GitHub

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    • Define functions under service of Node.js application
    • Check functions under metadata of oData service
    App Development Flow│ Consume ML Models in CAP Application
    • Implement a Node.js script to call HANA procedures
    (training & prediction)
    *Please find more details under this GitHub repo of sap-samples.

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    nSAP HANA Cloud | Predictive Analysis Library (PAL) – Key Capabilities
    Native In-Database Machine Learning with SAP HANA Cloud
    Predictive Analysis Library – Key capabilities
    § Addresses all key scenarios like Classification,
    Regression or Time Series Forecasting (and more)
    • All major machine learning scenario on structure data
    can be addressed, within the databases
    • Algorithms fast and optimized for in-database execution
    § Over 100 classic and trending algorithms
    • Random decision trees and gradient boosting
    decision trees outperform in most classification and
    regression use cases
    § High-performance parallel mass prediction, real-
    time transactional speed prediction
    • Multi-node fastest big data predictions as well as
    real-time transactional prediction in milliseconds
    § Segmented ML model development and prediction
    • Supported with all PAL algorithms and scenarios
    • Like segmented time series forecasting (forecast
    segmented by store, product, etc.)
    § Automated cross validation, hyper parameter
    selection and AutoML framework
    • Pipeline models and AutoML framework
    • Model development support and automation, higher
    productivity and faster results with best possible and
    stable models
    § Easy to develop and simple to embed within
    applications
    • Supports both expert data scientists and developer
    personas
    • Simple SQL interface and Python and R ML clients

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    SAP Roadmap Explorer – HANA ML / Predictive Analysis Library (PAL)
    § PAL with SAP HANA Cloud link
    Roadmap | SAP HANA Machine Learning
    With SAP HANA platform link
    Complete list of
    70 PAL enhancements
    with SAP HANA Cloud link
    SAP HANA 2 SPS07
    planned enhancements

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    SAP HANA Machine Learning product information overview page and documentation
    § SAP HANA Predictive Analysis Library (PAL) cloud documentation / on-premise documentation
    § Automated Predictive Library (APL) for SAP HANA documentation
    § Python machine learning client documentation here, @PyPI https://pypi.org/project/hana-ml/
    R machine learning client documentation here
    R / Python SAP HANA Machine Learning client install instructions
    § SAP HANA ML samples @github https://github.com/SAP-samples/hana-ml-samples
    § Intelligence out of the Box - Native Machine Learning in SAP HANA Cloud | SAP Community Call
    https://www.youtube.com/watch?v=bFv4n3smzQw
    Getting Started - Tutorials and Blogs with resource collection
    § https://blogs.sap.com/2020/08/03/getting-started-with-sap-hana-cloud-vi-machine-learning/
    § https://blogs.sap.com/2021/05/27/sap-hana-machine-learning-resources/
    SAP HANA | Embedded Machine Learning - Further Information

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    Business Application Studio / WebIDE Development
    § Collaborative Database Development in SAP HANA Cloud, SAP HANA Database | Tutorials for SAP Developers
    § Tech2021 SAP-samples/teched2021-DAT260: SAP TechEd session DAT260 (github.com)
    § https://blogs.sap.com/2020/12/21/modeling-in-business-application-studio-compared-to-sap-web-ide/
    § https://blogs.sap.com/2019/11/13/faq-modeling-in-web-ide/
    § Combine CAP with SAP HANA Cloud to Create Full-Stack Applications | Tutorials for SAP Developers
    § Capire - Multitenancy CAP applications, The hidden life of ServiceManager handled containers | SAP Blogs
    § Advanced HANA capabilties with CAP applications ./SAP-samples/cloud-cap-samples/tree/advanced-HANA-sample
    HANA Machine Learning XSA / BAS Application Integration
    § TechEd 2018 DAT364 Template: Developing smart applications using SAP HANA In-Database Machine Learning
    cmog/TechEd2018_DAT364 (github.com)
    § SAP Data & Analytics Showcase – Develop a HANA Machine Learning Application on SAP BTP
    and SAP BTP Data & Analytics Showcase – Overall Integration Demo
    BTP App Dev | Embeddeding HANA Machine Learning - Further informations

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  29. Thank you.
    Contact information:
    © 2022 SAP SE or an SAP affiliate company. All rights reserved. See Legal Notice on www.sap.com/legal-notice for use terms, disclaimers, disclosures, or restrictions related to SAP Materials for general audiences.
    [email protected]
    Christoph Morgen, SAP SE, Walldorf

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    So nutzen Sie SAP HANA
    effizient als Analytics-Plattform
    § Advanced Analytics, Machine Learning
    und vorausschauende Analysen
    § Praktische Beispiele für den Einsatz
    von PAL, APL und mehr
    § Für alle Betriebsformen von SAP HANA
    Data Science mit SAP HANA
    406 Seiten, gebunden,
    ab Ende Oktober 2022
    Buch | E-Book | Bundle
    ISBN 978-3-8362-9033-3
    Jetzt vorbestellen unter
    www.sap-press.de/5539

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  31. © 2020 SAP SE or an SAP affiliate company. All rights reserved.
    No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of
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    The information contained herein may be changed without prior notice. Some software products marketed by SAP SE and its
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    These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or
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