Skip to main content

33 posts tagged with "Roadmaps"

Stage-by-stage engineering journeys for production AI capabilities

View All Tags

The Complete Google Cloud Roadmap: Cloud Architecture, Data, AI Engineering, Security and Governance

· 81 min read
AI Playbook author

The AWS Learning Roadmap progresses from cloud fundamentals into identity, networking, compute, storage, databases, containers and serverless services. This Google Cloud roadmap follows the same learning sequence, but extends it into enterprise foundations, data engineering, generative AI, agentic AI, cybersecurity, governance, compliance, FinOps and production operations.

Terminology note: Google Cloud’s product naming is evolving, particularly around generative and agentic AI:

  • Cloud Functions capabilities are now presented as Cloud Run functions.
  • Dataplex Universal Catalog is now Knowledge Catalog.
  • Current Vertex AI Agent Engine documentation redirects to the Gemini Enterprise Agent Platform, which includes Agent Runtime, Sessions, Memory Bank, Sandbox, Agent Gateway, evaluation and governance capabilities.
  • Vertex AI continues to provide broader machine-learning capabilities such as model development, training, tuning, model management and deployment. (Google Cloud Documentation)

Model FinOps and AI Cost Engineering: An End-to-End Multi-Cloud Roadmap

· 33 min read
AI Playbook author

Model FinOps is the discipline of understanding, allocating, forecasting, governing and optimising the complete cost of building and operating AI systems. It extends traditional cloud FinOps because AI spending is not driven only by servers, storage and networking—it also depends on tokens, embeddings, retrieval, agents, training, dedicated capacity, evaluation and governance.

The FinOps Foundation frames FinOps for AI as addressing cost complexity, rapid development cycles, unpredictable consumption and the need to connect allocation and optimisation decisions to business value.

The objective is not simply to minimise AI spending. The objective is the best combination of business value, quality, reliability and security for an acceptable total cost, risk and operational complexity. A cheaper model that produces poor answers or requires extensive human correction may be more expensive overall than a more capable model.