{"ref":"artifact:sap-samples-teched2025-da261","page":"/artifacts/sap-samples-teched2025-da261","json":"/artifacts/sap-samples-teched2025-da261.json","generated":"2026-10-26","drafted_with_ai":true,"entity":{"id":"sap-samples-teched2025-da261","name":"teched2025-DA261","artifact_type":"sample","ecosystem":"btp","domain":"ai-and-agents","origin_ring":"sap-official","use_as":"reference","description":{"one_line":"An SAP TechEd 2025 hands-on workshop on [SAP HANA Cloud](artifact:btp-hana-cloud)'s AI capabilities: outlier analysis, text embeddings with machine learning, and the knowledge graph engine.","short":"TechEd 2025 workshop on SAP HANA Cloud's AI capabilities","what_it_solves":"Three main exercises, each worked in a Jupyter notebook from a Python environment in [SAP Business Application Studio](artifact:btp-business-application-studio) against a connected [SAP HANA Cloud](artifact:btp-hana-cloud) instance, with the Python Machine Learning client generating the SQL: outlier analysis on financial transaction data with Isolation Forests, including a data-parallel variant; consumer complaints explored with HANA dataframes and classified by an AutoML model that predicts a company's monetary relief; and RDF triples created, queried with SPARQL and validated with SHACL in the knowledge graph engine, which the README reads for SQL-SPARQL interoperability. Sentiment through text analysis and similarity search over the complaint narratives with the vector engine and the text embedding model are marked optional add-ons. Getting Started comes first and the three main exercises can then be taken in any order; the systems are the ones the in-person workshop provides."},"links":{"repo":"https://github.com/SAP-samples/teched2025-DA261"},"identity":{"license":"Apache-2.0"},"source_available":true,"type_properties":{"form":{"value":"workshop","sources":[{"url":"https://github.com/SAP-samples/teched2025-DA261","title":"SAP-samples/teched2025-DA261 — README, Description","checked":"2026-10-19","quote":"This repository contains the material for the SAP TechEd 2025 Hands-on Workshop __Unlocking AI-driven insights from your business data in SAP HANA Cloud__ (Session-ID DA261)."}]}},"demonstrates":["capability:retrieval-augmented-generation","capability:predictive-models","capability:multi-model-data"],"requires":[{"ref":"artifact:btp-hana-cloud","for":"the database every exercise runs against; the notebooks generate SQL for execution in the connected instance.","sources":[{"url":"https://github.com/SAP-samples/teched2025-DA261","title":"SAP-samples/teched2025-DA261 — README, Overview","checked":"2026-10-19","quote":"In this workshop you will learn how to utilize SAP HANA Cloud's AI capabilities for custom AI applications on SAP Business Technology Platform."},{"url":"https://github.com/SAP-samples/teched2025-DA261/blob/main/exercises/ex2/README.md","title":"SAP-samples/teched2025-DA261 — Exercise 2, Getting started","checked":"2026-10-19","quote":"You will mainly work with notebook scripts using the Python Machine Learning client for SAP HANA, which generates SQL for immediate execution within the connected SAP HANA Cloud instance."}]},{"ref":"artifact:btp-business-application-studio","for":"where the Python environment and the notebooks are; configuring it is part of the getting-started exercise.","sources":[{"url":"https://github.com/SAP-samples/teched2025-DA261","title":"SAP-samples/teched2025-DA261 — README, Exercises","checked":"2026-10-19","quote":"Python environment in SAP Business Application Studio"},{"url":"https://github.com/SAP-samples/teched2025-DA261/blob/main/exercises/ex2/README.md","title":"SAP-samples/teched2025-DA261 — Exercise 2, Getting started","checked":"2026-10-19","quote":"During this exercise, you will work with Jupyter Notebooks and a Python environment available within SAP Business Application Studio."}]}],"maintainers":["actor:sap-samples"],"drafted_with_ai":true,"provenance":{"discovered_via":[{"source":"source:curator"}],"first_seen":"2026-09-14"},"contributors":["actor:ajaysoreng","actor:cmog","actor:shabana31"]},"effective_normative_relations":[],"readings":{"liveness":[{"label":"Maintenance","tier":"unknown","evidence":"no release data"},{"label":"Continuity","tier":"good","evidence":"3 contributors, org-owned"},{"label":"Repository","tier":"good","evidence":"pushed 2026-06, not archived"}],"compatibility":[{"label":"Declared for","tier":"unknown","evidence":"no BTP release lines in the atlas"},{"label":"Survived majors","tier":"unknown","evidence":"no BTP release lines in the atlas"},{"label":"Maintainer's CI","tier":"fair","evidence":"no CI configured"}],"practice":[{"label":"Tests","tier":"fair","evidence":"none found in the tree"},{"label":"Docs","tier":"good","evidence":"README 5 KB"},{"label":"Changelog","tier":"fair","evidence":"none in the repo"}],"reach":[{"value":null,"unit":"installs/week"},{"value":8,"unit":"watching"}]},"signals":{"crawled_at":"2026-10-25","npm":"not published under this name","stars":8,"forks":1,"open_issues":0,"archived":false,"pushed_at":"2026-06-09","license_spdx":"Apache-2.0","org_owned":true,"default_branch":"main","created_at":"2025-08-07T11:35:55Z","open_prs":0,"tree_truncated":false,"test_files":0,"workflows":0,"other_ci":false,"readme_bytes":4797,"docs_files":0,"changelog":false,"license_file":true,"dts_files":0,"first_commit":"2025-08-07","last_commit":"2026-06-09","commits":69,"contributors":3,"readme_path":"README.md","readme_sha":"33970fab3768cd8e9cb91caae907149bbf18db82","repo_read":"2026-10-25"},"edges":{"maintainers":[{"ref":"actor:sap-samples","name":"SAP Samples","json":"/actors/sap-samples.json"}],"contributors":[{"ref":"actor:ajaysoreng","name":"Ajay Soreng","json":"/actors/ajaysoreng.json"},{"ref":"actor:cmog","name":"Christoph Morgen","json":"/actors/cmog.json"},{"ref":"actor:shabana31","name":"Shabana Samsudheen","json":"/actors/shabana31.json"}],"explained_by":[],"alternative_to_this":[],"needed_by":[],"available_via":[],"parts":[],"add_ons":[],"answered_through_parts":[]}}