Skip to main content

149 posts tagged with "Playbook"

Posts about the AI Playbook product and practice

View All Tags

The Complete Forward-Deployed Engineer Roadmap: From Full-Stack Developer to Customer-Facing Technical Leader

· 27 min read
AI Playbook author

A Forward-Deployed Engineer, commonly called an FDE, works at the intersection of software engineering, product development, consulting and customer delivery.

Unlike a conventional software engineer who may focus mainly on an internal product backlog, an FDE works directly with customers to understand their operations, translate ambiguous requirements into technical solutions, deploy those solutions into real environments and improve the core product based on what is learned in the field.

Frameworks for End-to-End AI Solution Engineering

· 22 min read
AI Playbook author

These frameworks provide structured approaches, tools and methodologies that streamline the development, deployment and management of AI systems. No single library or methodology covers the full journey from ambiguous business problem to governed production capability. End-to-end AI solution engineering therefore uses a stack: consulting and strategy frameworks to decide what to build; data, ML, MLOps and orchestration frameworks to build and run it; domain toolkits for specialised capabilities; and security–governance frameworks as gatekeepers that decide whether work may proceed.

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.

Build AI systems that survive the enterprise

· 3 min read
AI Playbook author

AI prototypes are easy to demonstrate. Enterprise AI solutions are significantly harder to design, secure, govern, integrate and scale.

This series is a practical guide for experienced AI engineers, cloud engineers, architects, consultants and technical leaders who want to move beyond isolated models and build complete enterprise AI capabilities.