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Systems design

From Wikipedia, the free encyclopedia

The basic study of system design is the understanding of component parts and their subsequent interaction with one another.[1]

Systems design has appeared in a variety of fields, including sustainability,[2] computer/software architecture,[3] and sociology.[4]

Product Development

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If the broader topic of product development "blends the perspective of marketing, design, and manufacturing into a single approach to product development,"[5] then design is the act of taking the marketing information and creating the design of the product to be manufactured.

Thus in product development, systems design involves the process of defining and developing systems, such as interfaces and data, for an electronic control system to satisfy specified requirements. Systems design could be seen as the application of systems theory to product development. There is some overlap with the disciplines of systems analysis, systems architecture and systems engineering.[6][7]

Physical design

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The physical design relates to the actual input and output processes of the system. This is explained in terms of how data is input into a system, how it is verified/authenticated, how it is processed, and how it is displayed. In physical design, the following requirements about the system are decided.

  1. Input requirement,
  2. Output requirements,
  3. Storage requirements,
  4. Processing requirements,
  5. System control and backup or recovery.[8]

Put another way, the physical portion of system design can generally be broken down into three sub-tasks:

  1. User Interface Design
  2. Data Design
  3. Process Design

Web System design

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Online websites, such as Google, Twitter, Facebook, Amazon and Netflix are used by millions of users worldwide. A scalable, highly available system must be designed to accommodate an increasing number of users. Here are the things to consider in designing the system:

  1. Functional and non functional requirements
  2. Capacity estimation
  3. Database to use, Relational or NoSQL
  4. Vertical scaling, Horizontal scaling, Shard
  5. Load Balancing
  6. Primary-secondary Replication
  7. Cache and CDN
  8. Stateless and Stateful servers
  9. Datacenter georouting
  10. Message Queue, Publish-Subscribe Architecture
  11. Performance Metrics Monitoring and Logging
  12. Build, test, configure deploy automation
  13. Finding single point of failure
  14. API Rate Limiting
  15. Service Level Agreement

Machine Learning Systems Design

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Machine learning systems design focuses on building scalable, reliable, and efficient systems that integrate machine learning (ML) models to solve real-world problems. ML systems require careful consideration of data pipelines, model training, and deployment infrastructure. ML systems are often used in applications such as recommendation engines, fraud detection, and natural language processing.

Key components to consider when designing ML systems include:

  1. Problem Definition: Clearly define the problem, data requirements, and evaluation metrics. Success criteria often involve accuracy, latency, and scalability.[9]
  2. Data Pipeline: Build automated pipelines to collect, clean, transform, and validate data.[10]
  3. Model Selection and Training: Choose appropriate algorithms (e.g., linear regression, decision trees, neural networks) and train models using frameworks like TensorFlow or PyTorch.
  4. Deployment and Serving: Deploy trained models to production environments using scalable architectures such as containerized services (e.g., Docker and Kubernetes).[11]
  5. Monitoring and Maintenance: Continuously monitor model performance, retrain as necessary, and ensure data drift is addressed.[12]

Designing an ML system involves balancing trade-offs between accuracy, latency, cost, and maintainability, while ensuring system scalability and reliability. The discipline overlaps with MLOps, a set of practices that unifies machine learning development and operations to ensure smooth deployment and lifecycle management of ML systems.

See also

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References

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  1. ^ Papanek, Victor J. (1984) [1972]. Design for the Real World: Human Ecology and Social Change (2nd ed.). Chicago: Academy Chicago. p. 276. ISBN 0897331532. OCLC 12343986.
  2. ^ Blizzard, Jacqualyn; Klotz, Leidy (2012). "A framework for sustainable whole systems design". R Design Studies. 33 (5): 456–479. doi:10.1016/j.destud.2012.03.001.
  3. ^ Lukosh, Heidi; Bekebrede, Geertje; Kurapati, Shalini; Lukosch, Stephan (2018). "A Scientific Foundation of Simulation Games for the Analysis and Design of Complex Systems". Simulation & Gaming. 49 (3): 279–314. doi:10.1177/1046878118768858. PMC 6187265. PMID 30369775.
  4. ^ Werner, Ulrich (September 1987). "Critical heuristics of social systems design". European Journal of Operational Research. 31 (3): 276-283. doi:10.1016/0377-2217(87)90036-1.
  5. ^ Ulrich, Karl T.; Eppinger, Steven D. (2000). Product Design and Development (Second ed.). Boston: Irwin McGraw-Hill.
  6. ^ Public Domain This article incorporates public domain material from Federal Standard 1037C. General Services Administration. Archived from the original on 2022-01-22.
  7. ^ Public Domain This article incorporates public domain material from Dictionary of Military and Associated Terms. United States Department of Defense.
  8. ^ Arden, Trevor (1991). Information technology applications. London: Pitman. ISBN 978-0-273-03470-4.
  9. ^ Sorvisto, Dayne (2023). MLOps Lifecycle Toolkit: A Software Engineering Roadmap for Designing, Deploying, and Scaling Stochastic Systems. Apress. ISBN 978-1-4842-9641-7.
  10. ^ Polyzotis, Neoklis (2018). "Data Management Challenges in Production Machine Learning". Proceedings of the 2017 ACM SIGMOD International Conference on Management of Data. doi:10.1145/3035918.3054782.
  11. ^ Huyen, Chip (2022). Designing Machine Learning Systems. O'Reilly Media. ISBN 978-1-098-10796-3.
  12. ^ "Machine Learning at Scale: Challenges and Best Practices". Google Cloud Blog. 2020.

Further reading

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Look up systems design in Wiktionary, the free dictionary.
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