SAP HANA Automated Predictive Library (APL)

What APL automates, and how SAP's own code proves it

The Automated Predictive Library (APL) is SAP HANA's automated-machine-learning counterpart to PAL: both are AFL — Application Function Library — components, both compute inside HANA, but APL trims the surface a caller has to configure. Where a PAL classification call passes an explicit parameter table naming an algorithm and its hyperparameters, SAP's own APL sample code for a classification model sets three configuration entries and lets APL do the rest: APL/ModelType ("binary classification"), APL/CuttingStrategy ("random with no test"), and — the one that matters most — APL/VariableAutoSelection set to true. That single flag is the concrete, documented mechanism behind the word "automated" in APL's name: the library decides which of the input variables actually belong in the model, rather than a data scientist deciding it upfront.

APL runs inside its own schema, SAP_PA_APL, with dedicated table types for models, logs, summaries and debrief metrics (MODEL_BIN_OID, OPERATION_LOG, SUMMARY, DEBRIEF_METRIC_OID, DEBRIEF_PROPERTY_OID) — the schema name alone is worth recognizing in a client's system as evidence APL is installed and in use.

Why it matters

• APL/VariableAutoSelection is the concrete, documented mechanism behind "automated" in APL's name — a consultant can point to this specific flag instead of describing automation vaguely.
• APL's function set is materially broader than the three scenarios Smart Predict exposes in the SAC UI: recommenders, social-network modeling and data-drift monitoring are APL capabilities without a direct Smart Predict UI equivalent.
• SAP names APL directly as what Smart Predict delegates to for live on-premise HANA connections — the one place in the documentation that ties “SAC Smart Predict — Classification, Regression, Time Series”, “SAP HANA Predictive Analysis Library (PAL)” and “SAP HANA Automated Predictive Library (APL)” together concretely.

Key points

• APL is HANA's automated-ML AFL component; SAP's own sample code sets APL/ModelType, APL/CuttingStrategy and APL/VariableAutoSelection=true instead of naming an algorithm and tuning parameters.
• APL runs in its own SAP_PA_APL schema with dedicated table types for models, logs, summaries and debrief metrics.
• Predictive model functions (lifecycle): CREATE_MODEL(_AND_TRAIN), TRAIN_MODEL, RETRAIN_MODEL, UPDATE_MODEL, APPLY_MODEL(_AND_TEST), TEST_MODEL, PUBLISH_MODEL, EXPORT_APPLY_CODE, GET_MODEL_INFO/DEBRIEF, GET_VARIABLE_INDICATORS.
• Predictive business functions (scenario coverage): FORECAST/FORECAST_AND_DEBRIEF (time series), KEY_INFLUENCERS, RECOMMEND family (recommenders), CREATE_SOCIAL_MODEL_AND_TRAIN/APPLY_SOCIAL_MODEL (social-network modeling), PROFILE_DATA family, SCORING_EQUATION.
• Data drift detection is a distinct, documented APL capability with learn/report and detect/report examples, including segmentation by a dimension like country.
• EXPORT_APPLY_CODE lets APL generate scoring code deployable outside HANA — but the automated variable-selection decisions behind it remain APL's.
• Called via direct SQL against AFL procedures, or via hana-ml's hana_ml.algorithms.apl module in Python — both paths run the same automated logic.
• SAP's own Smart Predict live-connectivity documentation states execution is delegated to APL for on-premise live connections — the one place this dependency is named directly.

Terms used on this page

APL/VariableAutoSelection

The APL configuration flag documented in SAP's own sample code that turns on automated feature/variable selection.

SAP_PA_APL

The dedicated HANA schema APL runs in, with its own model, log, summary and debrief table types.

EXPORT_APPLY_CODE

APL function that generates scoring code deployable outside SAP HANA, based on a trained APL model.

Data drift (APL)

APL's dedicated capability to learn a baseline distribution and detect and report deviation from it, optionally segmented by a dimension.

SCORING_EQUATION

APL function that exports a trained model as a portable scoring formula.

Sources

1. SAP HANA Automated Predictive Library — What is SAP HANA Automated Predictive Library? (accessed 2026-09-25)
2. SAP HANA Automated Predictive Library — crawler-rendered doc index (What's New, Installation, Developer Guide), version 2219, fetched 2026-09-25
3. SAP HANA Automated Predictive Library Developer Guide — full function reference table of contents (fetched 2026-09-25)
4. SAP-samples/hana-ml-samples — APL-SQL sample directory (Monthly/Daily Forecast, Classification, Regression, Data Drift) — GitHub (SAP), fetched 2026-09-25
5. SAP-samples/hana-ml-samples — 31_Classification-Learn_and_Report.sql (APL/ModelType, APL/VariableAutoSelection) — GitHub (SAP), fetched 2026-09-25
6. SAP Analytics Cloud Help — Q&A: Smart Predict with Live SAP HANA Data (APL dependency for live connectivity) — accessed 2026-09-25
7. SAP Help Portal — Generative AI hub orchestration service overview (accessed 2026-09)
8. SAP Help Portal — Generative AI hub overview (accessed 2026-09)
9. news.sap.com — TabPFN-3.5-Plus now available in SAP AI Core (2026-09-15)
10. Prior Labs — TabPFN 3.5 changelog (accessed 2026-09)
11. SAP HANA Automated Predictive Library Developer Guide (2025-06-20) — SAP Help Portal
12. hana_ml.algorithms.apl package (hana-ml 2.27.251222) — SAP Help Portal
13. SAP x AI/ML Series: ISLM Embedded Scenario with APL — SAP Community
14. SAP-samples/hana-ml-samples — SAP on GitHub