42 posts tagged with "Governance"

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 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)