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Vitaliy Rudnytskiy
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hana-ml-sampleshana-ml-samplesSAP-authored code examples for predictive and machine learning scenarios on SAP HANA, from Predictive Analysis Library SQL to hana-ml Python notebooks.sap official, btp
Code examples for predictive and ML scenarios on SAP HANA
SAP-samples/codejam-cap-llmSAP-samples/codejam-cap-llmA CodeJam that builds a CAP-based job posting service exposing SAP AI Core's generative AI capabilities, including vector embeddings, through an OData API.sap official, cap
CodeJam on a CAP job posting service using SAP AI Core over OData
SAP-samples/codejam-code-based-agentsSAP-samples/codejam-code-based-agentsA CodeJam teaching code-based AI agents in Python or TypeScript, from a first agent through custom tools to deployment on SAP BTP and integration with Joule.sap official, btp
CodeJam on code-based AI agents in Python or TypeScript on SAP BTP
SAP-samples/generative-ai-codejamSAP-samples/generative-ai-codejamA CodeJam that deploys and queries a large language model on SAP AI Core's Generative AI Hub, then builds a retrieval-augmented chatbot on top of it.sap official, btp
CodeJam deploying an LLM on Generative AI Hub and building RAG
SAP-samples/hana-ml-py-codejamSAP-samples/hana-ml-py-codejamSAP CodeJam material that walks through a first machine learning workflow in SAP HANA Cloud from Python, in Jupyter notebooks from HANA DataFrames to hyperparameter tuning and AutoML.sap official, btp
CodeJam on a first machine learning workflow in SAP HANA Cloud
SAP-samples/teched2025-DA260SAP-samples/teched2025-DA260A TechEd 2025 workshop covering SAP Business Data Cloud's connectivity options across SAP Analytics Cloud and SAP Datasphere, by replication, federation and push alike.sap official, btp
TechEd 2025 workshop on SAP Business Data Cloud connectivity options
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Combining AI Agents with the power of SAP HANA embedded ML (part 1)Combining AI Agents with the power of SAP HANA embedded ML (part 1)Devtoberfest 2026 session DAT-12, the first of two parts, in which Vitaliy Rudnytskiy does a data scientist's work against the embedded machine learning of SAP HANA Cloud by talking to an agent. The session first takes an agent apart into its LLM, its tools, its memory and the loop between them, then stacks the Predictive Analysis Library under the hana-ml Python wrapper and that under the HANA AI toolkit, whose tools an agent calls from plain English in a notebook to preview a sales refunds table, split it into train and predict tables, run a time-series check and train an AutoML forecast, keeping what it decided in Markdown memory files. That is the agent tooling edge; the same toolkit is then started as an MCP server over stdio and over streamable HTTP and reached from a coding agent and from Joule Work's desktop, which is where the live demo partly fails and the failure is shown.published 2026-09-22
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