metodologia-ai-pipeline-architecture

What problem does it solve?

AI pipeline architecture designs the end-to-end blueprint for building and operating AI systems, aligning development and production pipelines, data stores, model registries, and CI/CD.

Core Features & Use Cases

• Development-to-production pipeline design covering data ingestion, transformation, model training, deployment, and monitoring.
• Data store strategy across relational, object, vector, and cache stores with governance implications.
• Model registry design, versioning, lineage, and promotion gates for safe deployment.
• CI/CD practices for AI workloads (Blue and Gold), validation gates, and rollback capabilities.
• Use cases include planning, evaluating trade-offs, and documenting the architecture to accelerate delivery across teams.

Quick Start

Map your current AI workflows to a two-pipeline model (development and production) and outline data-store choices, registry workflow, and CI/CD approach.