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SAP HANA Machine Learning Python Client (hana-ml) development skill.
Passed 22 of 22 test cases with the skill, and 20 of 22 without it.
2 fixed20 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
While training in-database models using SAP HANA ML in Python, I want to catch exceptions specific to the HANA ML client rather than generic Python exceptions or SQLAlchemy errors. Which exception class should be imported and caught?
| Checked | Without skill | With skill |
|---|---|---|
| Imports Error from hana_ml.ml_exceptions. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
| Issue | First check |
|---|---|
| Connection fails | Verify HANA host, port, TLS/encryption, user privileges, and network allowlists. |
| PAL/APL algorithm missing | Confirm the HANA system has the required AFL/PAL/APL libraries installed and licensed. |
| DataFrame collection is slow | Push filtering/projection into HANA and avoid collecting large frames into Python. |
Package Version: 2.22.241011
Last Verified: 2025-11-27
pip install hana-ml
Requirements: Python 3.8+, SAP HANA 2.0 SPS03+ or SAP HANA Cloud
test cases passed, vs 20 / 22 without
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