Jupyter Notebook ML Workflows
The concept to be defined is not specified. Based on the provided sources, which cover topics such as SAP HANA Cloud graph workspaces, machine learning with AutoML, Service Ticket Intelligence, SAP Signavio process optimization, and Python connectivity, a meaningful definition cannot be accurately constructed without knowing the specific concept in question. Please provide the concept name so a precise, source-grounded definition can be written.
Tutorials that teach this
- Tutorial Analyze Graph Workspace in SAP HANA Cloud, SAP HANA Database
- Tutorial AI-driven Process Optimization: Run Machine Learning use cases in SAP Signavio leveraging SAP Build
- Tutorial Use Service Ticket Intelligence and Jupyter Notebook to Classify Service Requests
- Tutorial Use Service Ticket Intelligence and Jupyter Notebook to generate Clusters and Keywords in Service Requests
- Tutorial Train Your First Machine Learning Model Using AutoML in SAP HANA Cloud
- Tutorial Connect SAP HANA Database in SAP HANA Cloud to Python
- Tutorial Use Service Ticket Intelligence and Jupyter Notebook to Get Solution Recommendations
Code samples embodying this
Prerequisites
- Concept SAP AI Core Service Setup The concept name was not provided, so a precise definition cannot be constructed from the available sources. Based on the sources, these materials collectively cover building and integrating [Generative AI applications](https://architecture.learning.sap.com/docs/golden-path/ai-golden-path/2-build-and-deliver/3-genai-applications/readme) on SAP Business Technology Platform using SAP AI Core — a managed service that developers use to train models, run AI workloads, and connect to large language models via orchestration. Developers leverage it alongside [SAP BTP service instances and keys](https://help.sap.com/docs/btp/sap-business-technology-platform/5b35ee98403045309bb0f8c7f0c79365?locale=en-US&state=PRODUCTION&version=Cloud) to authenticate and consume AI capabilities such as prompt optimization, grounding, multimodal responses, and custom evaluation within their applications.
- Concept Business Application Studio (BAS) App Development The provided sources do not contain sufficient information to define this concept. The concept name is listed as "undefined," and none of the source snippets supply grounded, substantive content that could be used to write an accurate 2–4 sentence definition. Please provide a valid concept name and supporting source material.
- Concept Jupyter Notebook Basics The concept provided is **undefined**, and the supplied sources do not describe or name a specific SAP developer concept to define. The sources cover topics such as machine learning primers, SAP AI Core tooling, SAP HANA Cloud spatial data visualization, and Service Ticket Intelligence workflows using Jupyter Notebook, but no single named concept is identified for definition.
- Concept Python for Data Science The concept name was not provided, so a precise definition cannot be generated. Please supply a valid concept name along with the relevant source snippets so that an accurate, source-grounded definition can be written.
Concepts that build on this
- Concept Service Ticket Intelligence
- Concept Data Analysis and Visualization
- Concept Python Machine Learning Client for SAP HANA (hana-ml)
- Concept Predictive Modeling and Model Training
- Concept SAP HANA Graph Algorithms (Neighbors, Shortest Path)
- Concept Generative AI Custom Evaluation in SAP AI Core