Jul 12, 2024 · Learn about a single deployable set of repeatable and maintainable patterns for creating machine learning CI/CD and retraining pipelines.

Apr 30, 2024 · Machine learning architecture is the structure and organisation of the many components and processes that are part of a machine learning system. It defines how you process data, train and evaluate ML models, and generate predictions.

The cloud agnostic architecture diagrams in this paper provide high-level best practices with the following assumptions: All concepts presented here are cloud and technology agnostic. Solid black lines are indicative of process flow.

Feb 13, 2025 · What is a Machine Learning Architecture Diagram? Machine learning architecture refers to the structure and organization of all the components and processes that make up a machine learning system, from data preparation for machine learning applications to their deployment and maintenance.

Jan 1, 2022 · This research concludes an overall summary of the publications so far on the applied Machine Learning (ML) techniques in different phases of Software Development Life Cycle (SDLC) that...

Jul 15, 2021 · Though ML/DL program is also a type of software, its development lifecycle does not follow the conventional Software Development Lifecycle (SDLC) process. Though the lifecycle processes are...

Understanding the Components of a machine learning architecture involves familiarizing oneself with each part of the system and its role. This knowledge helps in designing robust and efficient models. For example, knowing how data flows through the system and the purpose of each component aids in troubleshooting and optimizing the architecture.

Feb 3, 2025 · Azure Architecture Center provides example architectures, architecture guides, architectural baselines, and ideas that you can apply to your scenario. Workloads that involve AI and machine learning components should follow the Azure Well-Architected Framework AI workloads guidance.

In this paper, software-engineering architecture and design (anti-)patterns for ML application systems are analyzed to bridge the gap between traditional software systems and ML application systems with respect to architecture and design.

Explore essential machine learning architecture diagrams to understand complex systems and improve your ML projects. Machine learning architecture diagrams serve as visual representations of the various components and processes involved in machine learning systems.