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An enterprise cloud architecture team is preparing for a large-scale workload migration to Google Cloud. The team has accumulated extensive, unstructured technical documentation across multiple systems, including legacy workload inventories, network restriction matrices, compliance guidelines, licensing terms, and disaster recovery runbooks.
The team needs to quickly synthesize these disparate documents into a centralized, interactive knowledge base. The solution must enable architects to analyze multi-workload dependencies, identify cross-system constraints, and generate evidence-based migration recommendations grounded strictly in their curated source materials with verifiable citations.
Which solution should the cloud architect implement?
Create a centralized NotebookLM workspace, import the heterogeneous technical documentation as curated sources, and query the notebook to synthesize architectural dependencies and generate citation-backed migration insights.
Deploy Cortex Framework Data Mesh and Dataplex Universal Catalog to tag BigQuery metadata tables and generate infrastructure migration plans.
Upload all migration documentation into a Cloud Storage bucket and configure Eventarc triggers with Cloud Workflows to parse text and execute automated remediation.
Provision a Vertex AI Workbench instance with TPU accelerators to fine-tune an open-source Gemma language model directly on the unstructured documents.
Create a centralized NotebookLM workspace, import the heterogeneous technical documentation as curated sources, and query the notebook to synthesize architectural dependencies and generate citation-backed migration insights.
NotebookLM is an AI-powered personalized knowledge assistant built on Google's advanced language models. It functions as a specialized knowledge synthesis environment designed to ground generative capabilities directly within user-provided source materials, such as technical specifications, architecture plans, runbooks, and audit notes.
NotebookLM provides immediate, turnkey synthesis of complex, unstructured documentation with built-in grounding and citations, making it the fastest and most reliable tool for evidence-based cloud migration planning.
Deploy Cortex Framework Data Mesh and Dataplex Universal Catalog to tag BigQuery metadata tables and generate infrastructure migration plans.
Upload all migration documentation into a Cloud Storage bucket and configure Eventarc triggers with Cloud Workflows to parse text and execute automated remediation.
Provision a Vertex AI Workbench instance with TPU accelerators to fine-tune an open-source Gemma language model directly on the unstructured documents.
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