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The Breakthrough Method of Agile AI Driven Development (BMAD) defines a technological setup combining automated model integration, CI/CD for ML artifacts and developer-centric feedback loops. It orchestrates data pipelines, governance and test automation to embed ML artifacts repeatably into agile workflows. The goal is faster, controlled releases of AI-powe…
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BMAD is a technology-oriented method that embeds AI models into agile development and deployment pipelines so that model delivery, testing, and feedback loops can run repeatedly.
BMAD combines patterns from agile software development, MLOps, and platform automation. It addresses the practical problem that AI models are often created separately from code, data, and release gates, which makes them hard to reproduce in delivery pipelines. The method arranges training, registration, testing, and deployment into a controlled flow for AI-powered features.
Think of BMAD as a production line with quality gates. On the left, data and model artifacts are created. Apache Airflow plans and coordinates jobs, MLflow records experiments and model versions, GitHub Actions runs checks and builds, and Kubeflow deploys workflows onto Kubernetes. Governance and test stages sit before release; operational feedback returns to the next iteration.
Experiments, parameters, and model versions are managed in a traceable way.
Data and model jobs are scheduled and monitored as workflows.
Builds, checks, and release steps can be automated inside development workflows.
Machine learning workflows are deployed and operated on Kubernetes.
The container runtime and scaling layer provides the operational base for the workflows.
Training data, models, pipelines, and configuration are treated as versionable delivery items.
BMAD is useful when AI features must be delivered regularly and teams want to align model training, release approval, and operations. It supports reproducibility, auditability, and controlled releases, but it also adds process and platform overhead. Without disciplined versioning of data, code, and models, plus a stable runtime environment, the benefit stays limited.
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