What is the sap-hana-ml Claude Code skill?

It is a specialized capability for Claude Code that provides domain-specific guidance, code patterns, and best practices for developing machine learning models using the SAP HANA Python Client (hana-ml).

Does this skill support automated machine learning?

Yes, it includes comprehensive support for SAP's Automated Predictive Library (APL), enabling automated classification, regression, and feature engineering workflows.

How does this skill handle large datasets?

It promotes best practices like lazy evaluation and in-database processing, ensuring that heavy computations are performed within SAP HANA rather than pulling large datasets into the Python environment.

Can I use this for data visualization?

Absolutely. The skill covers 14 visualization modules for exploratory data analysis (EDA), SHAP model interpretability, and performance metrics.

SAP HANA Machine Learning (hana-ml)

Name: SAP HANA Machine Learning (hana-ml)
Author: secondsky

bysecondsky

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데이터 과학 및 ML

Develops and deploys in-database machine learning models using the SAP HANA Python Client for PAL, APL, and AutoML workflows.

소개

This skill empowers Claude to assist developers in building production-ready machine learning solutions directly within the SAP HANA ecosystem. It provides expert guidance on using the hana-ml library for in-database processing, leveraging the Predictive Analysis Library (PAL) and Automated Predictive Library (APL) for high-performance analytics without moving data. Claude can help with lazy-evaluation DataFrame operations, automated feature engineering, model persistence via ModelStorage, and advanced visualization techniques, ensuring efficient and scalable AI implementation on SAP HANA and SAP HANA Cloud.

주요 기능

In-database machine learning using 100+ PAL and APL algorithms
Automated ML (AutoML) for classification, regression, and time series
11 GitHub stars
Model persistence and lifecycle management with ModelStorage
Advanced data visualization and model interpretability with SHAP integration
Lazy evaluation DataFrame operations for high-performance data processing

사용 사례

Optimizing data pipelines using in-database preprocessing and feature selection
Implementing automated forecasting and time-series analysis for financial planning
Building predictive maintenance models directly on SAP HANA business data

소개

주요 기능

In-database machine learning using 100+ PAL and APL algorithms
Automated ML (AutoML) for classification, regression, and time series
11 GitHub stars
Model persistence and lifecycle management with ModelStorage
Advanced data visualization and model interpretability with SHAP integration
Lazy evaluation DataFrame operations for high-performance data processing

사용 사례

Optimizing data pipelines using in-database preprocessing and feature selection
Implementing automated forecasting and time-series analysis for financial planning
Building predictive maintenance models directly on SAP HANA business data