MCPInstall

sap-hana-ml

by secondskyAI & Agents

SAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using Python hana-ml library for PAL/APL algorithms, DataFrame operations, AutoML, model persistence, and visualization. Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics, HANA DataFrame, ConnectionContext, classification, regression, clustering, time series, ARIMA, gradient boosting, AutoML, SHAP, model storage

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/secondsky/sap-skills --skill sap-hana-ml
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads sap-hana-ml automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

SAP HANA ML Python Client (hana-ml)

Related Skills

  • sap-dependency-security: Use for secure dependency pinning and upgrade workflows in Python/auxiliary tooling used alongside HANA ML stacks

When to Use This Skill

Use this skill when building machine learning workflows with the hana-ml Python client, using PAL/APL algorithms, querying HANA DataFrames, training or scoring models in-database, using AutoML, visualizing model output, or troubleshooting Python-to-HANA ML connections.

Common Issues

IssueFirst check
Connection failsVerify HANA host, port, TLS/encryption, user privileges, and network allowlists.
PAL/APL algorithm missingConfirm the HANA system has the required AFL/PAL/APL libraries installed and licensed.
DataFrame collection is slowPush filtering/projection into HANA and avoid collecting large frames into Python.

Package Version: 2.22.241011
Last Verified: 2025-11-27

Table of Contents

Installation & Setup

pip install hana-ml

Requirements: Python 3.8+, SAP HANA 2.0 SPS03+ or SAP HANA Cloud

Quick Start

Connection & DataFrame

from hana_ml import ConnectionContext

# Connect
conn = ConnectionContext(
    address='<hostname>',
    port=443,
    user='<username>',
    password='<password>',
    encrypt=True
)

# Create DataFrame
df = conn.table('MY_TABLE', schema='MY_SCHEMA')
print(f"Shape: {df.shape}")
df.head(10).collect()

PAL Classification

from hana_ml.algorithms.pal.unified_classification import UnifiedClassification

# Train model
clf = UnifiedClassification(func='RandomDecisionTree')
clf.fit(train_df, features=['F1', 'F2', 'F3'], label='TARGET')

# Predict & evaluate
predictions = clf.predict(test_df, features=['F1', 'F2', 'F3'])
score = clf.score(test_df, features=['F1', 'F2', 'F3'], label='TARGET')

APL AutoML

from hana_ml.algorithms.apl.classification import AutoClassifier

# Automated classification
auto_clf = AutoClassifier()
auto_clf.fit(train_df, label='TARGET')
predictions = auto_clf.predict(test_df)

Model Persistence

from hana_ml.model_storage import ModelStorage

ms = ModelStorage(conn)
clf.name = 'MY_CLASSIFIER'
ms.save_model(model=clf, if_exists='replace')

Core Libraries

PAL (Predictive Analysis Library)

  • 100+ algorithms executed in-database
  • Categories: Classification, Regression, Clustering, Time Series, Preprocessing
  • Key classes: UnifiedClassification, UnifiedRegression, KMeans, ARIMA
  • See: references/PAL_ALGORITHMS.md for complete list

APL (Automated Predictive Library)

  • AutoML capabilities with automatic feature engineering
  • Key classes: AutoClassifier, AutoRegressor, GradientBoostingClassifier
  • See: references/APL_ALGORITHMS.md for details

DataFrames

  • Lazy evaluation - builds SQL until collect() called
  • In-database processing for optimal performance
  • See: references/DATAFRAME_REFERENCE.md for complete API

Visualizers

  • EDA plots, model explanations, metrics
  • SHAP integration for model interpretability
  • See: references/VISUALIZERS.md for 14 visualization modules

Common Patterns

Train-Test Split

from hana_ml.algorithms.pal.partition import train_test_val_split

train, test, val = train_test_val_split(
    data=df,
    training_percentage=0.7,
    testing_percentage=0.2,
    validation_percentage=0.1
)

Feature Importance

# APL models
importance = auto_clf.get_feature_importances()

# PAL models
from hana_ml.algorithms.pal.preprocessing import FeatureSelection
fs = FeatureSelection()
fs.fit(train_df, features=features, label='TARGET')

Pipeline

from hana_ml.algorithms.pal.pipeline import Pipeline
from hana_ml.algorithms.pal.preprocessing import Imputer, FeatureNormalizer

pipeline = Pipeline([
    ('imputer', Imputer(strategy='mean')),
    ('normalizer', FeatureNormalizer()),
    ('classifier', UnifiedClassification(func='RandomDecisionTree'))
])

Best Practices

  1. Use lazy evaluation - Operations build SQL without execution until collect()
  2. Leverage in-database processing - Keep data in HANA for performance
  3. Use Unified interfaces - Consistent APIs across algorithms
  4. Save models - Use ModelStorage for persistence
  5. Explain predictions - Use SHAP explainers for interpretability
  6. Monitor AutoML - Use PipelineProgressStatusMonitor for long-running jobs

Bundled Resources

Reference Files

  • references/DATAFRAME_REFERENCE.md (479 lines)

    • ConnectionContext API, DataFrame operations, SQL generation

  • references/PAL_ALGORITHMS.md (869 lines)

    • Complete PAL algorithm reference (100+ algorithms)
    • Classification, Regression, Clustering, Time Series, Preprocessing

  • references/APL_ALGORITHMS.md (534 lines)

    • AutoML capabilities, automated feature engineering
    • AutoClassifier, AutoRegressor, GradientBoosting classes

  • references/VISUALIZERS.md (704 lines)

    • 14 visualization modules (EDA, SHAP, metrics, time series)
    • Plot types, configuration, export options

  • references/SUPPORTING_MODULES.md (626 lines)

    • Model storage, spatial analytics, graph algorithms
    • Text mining, statistics, error handling

Error Handling

from hana_ml.ml_exceptions import Error

try:
    clf.fit(train_df, features=features, label='TARGET')
except Error as e:
    print(f"HANA ML Error: {e}")

Documentation

FAQ

What does the sap-hana-ml skill do?

SAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using Python hana-ml library for PAL/APL algorithms, DataFrame operations, AutoML, model persistence, and visualization. Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics, HANA DataFrame, ConnectionContext, classification, regression, clustering, time series, ARIMA, gradient boosting, AutoML, SHAP, model storage

How do I install sap-hana-ml?

Run npx skills add https://github.com/secondsky/sap-skills --skill sap-hana-ml in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sap-hana-ml/ (personal) or .claude/skills/sap-hana-ml/ (project).

Which AI agents can use sap-hana-ml?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is sap-hana-ml safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains sap-hana-ml?

It is published by secondsky in secondsky/sap-skills. It has 652 recorded installs.

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