Generative AI with SAP and Amazon Bedrock Utilizing GenAI with SAP and AWS Business Use Cases (Miguel Figueiredo)（Z-Library）

【One-Line Pitch】 A practical, hands-on guide for SAP practitioners and enterprise developers who want to understand Generative AI fundamentals and build real GenAI use cases by combining SAP Business Technology Platform (BTP) with Amazon Bedrock and AWS SDK for ABAP. 【Book Arc】 - **Opening (~0%–15%)**: Introduces core AI and GenAI concepts, terminology, and the landscape of large language models (LLMs), establishing a baseline for readers new to the field. - **Early (~15%–35%)**: Explores industry-specific opportunities and impacts of GenAI, helping readers map abstract AI capabilities to concrete business problems and workflows. - **Middle (~35%–60%)**: Dives into SAP AI Core and the SAP Business Technology Platform, explaining how to set up cloud accounts and create the first GenAI artifacts within the SAP ecosystem. - **Late (~60%–85%)**: Shifts to Amazon Bedrock and AWS SDK for ABAP, showing how to integrate AWS's managed GenAI stack with SAP systems for practical deployment. - **Ending (~85%–100%)**: Consolidates step-by-step project guidance, emphasizing accelerated development skills and productivity gains for implementing GenAI use cases in enterprise environments. 【Key Takeaways】 - **GenAI is a business enabler, not just a tech trend** (Early): The book frames Generative AI as a tool for solving enterprise problems, not a novelty, urging readers to think in terms of use cases and ROI. - **LLMs are the core engine** (Early): Understanding how large language models work—training, fine-tuning, and inference—is essential before touching any SAP or AWS service. - **SAP BTP is the integration hub** (Middle): SAP AI Core within the Business Technology Platform serves as the central place to build, deploy, and manage AI artifacts alongside SAP data and processes. - **Amazon Bedrock simplifies GenAI adoption** (Late): The book positions Bedrock as a managed service that reduces the heavy lifting of running models, letting developers focus on application logic rather than infrastructure. - **AWS SDK for ABAP bridges two worlds** (Late): A key practical skill is using the AWS SDK within ABAP to call Bedrock APIs directly from SAP systems, enabling seamless AI-powered workflows. - **Step-by-step setup is critical** (Middle–Late): Establishing a cloud SAP BTP account and creating your first GenAI artifacts is a prerequisite skill; the book treats this as foundational, not optional. - **Productivity gains come from practice** (Ending): The ultimate goal is accelerating development—readers are encouraged to build small projects early to internalize the patterns rather than just reading theory. 【Reading Tips】 - **Skim the early AI theory if you're experienced**: Chapters on GenAI concepts and LLM basics are useful for beginners but can be skimmed by those already familiar with machine learning terminology. - **Deep-read the SAP BTP and Bedrock setup sections**: The step-by-step account creation and artifact-building instructions are the book's core value—follow them with a live environment if possible. - **Focus on the AWS SDK for ABAP integration**: This is the most distinctive and practical part; treat it as the payoff of the book and allocate extra time to understand the API calls and data flow. - **Use the industry use cases as a filter**: When reading about GenAI opportunities, pick the examples closest to your domain (e.g., supply chain, HR, finance) and ignore the rest to avoid overload. - **Take away the project template, not just facts**: The book's real takeaway is a repeatable pattern—set up BTP, connect Bedrock, call via ABAP—so aim to replicate that skeleton in your own environment. 【Coverage Limits】 The excerpts cover the book's overall structure and stated goals but do not include detailed chapter-level content, specific code samples, or step-by-step technical instructions. This guide synthesizes the book's positioning and learning path rather than its full technical depth.