Systems design

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The basic study of system design is the understanding of component parts and their subsequent interaction with one another.<ref>Template:Cite book</ref>

Systems design has appeared in a variety of fields, including sustainability,<ref>Template:Cite journal</ref> computer/software architecture,<ref>Template:Cite journal</ref> and sociology.<ref>Template:Cite journal</ref>

Product DevelopmentEdit

If the broader topic of product development "blends the perspective of marketing, design, and manufacturing into a single approach to product development,"<ref>Template:Cite book</ref> 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.<ref>Template:FS1037C</ref><ref>Template:DODDIC</ref>

Physical designEdit

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.

Input requirement,
Output requirements,
Storage requirements,
Processing requirements,
System control and backup or recovery.<ref>Template:Cite book</ref>

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

User Interface Design
Data Design
Process Design

Architecture designEdit

Designing the overall structure of a system focuses on creating a scalable, reliable, and efficient system. For example, services like Google, Twitter, Facebook, Amazon, and Netflix exemplify large-scale distributed systems. Here are key considerations:

Functional and non-functional requirements
Capacity estimation
Usage of relational and/or NoSQL databases
Vertical scaling, horizontal scaling, sharding
Load balancing
Primary-secondary replication
Cache and CDN
Stateless and Stateful servers
Datacenter georouting
Message Queue, Publish-Subscribe Architecture
Performance Metrics Monitoring and Logging
Build, test, configure deploy automation
Finding single point of failure
API Rate Limiting
Service Level Agreement

Machine Learning Systems DesignEdit

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:

Problem Definition: Clearly define the problem, data requirements, and evaluation metrics. Success criteria often involve accuracy, latency, and scalability.<ref>Template:Cite book</ref>
Data Pipeline: Build automated pipelines to collect, clean, transform, and validate data.<ref>Template:Cite book</ref>
Model Selection and Training: Choose appropriate algorithms (e.g., linear regression, decision trees, neural networks) and train models using frameworks like TensorFlow or PyTorch.
Deployment and Serving: Deploy trained models to production environments using scalable architectures such as containerized services (e.g., Docker and Kubernetes).<ref>Template:Cite book</ref>
Monitoring and Maintenance: Continuously monitor model performance, retrain as necessary, and ensure data drift is addressed.<ref>{{#invoke:citation/CS1|citation

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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.

ReferencesEdit

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External linksEdit

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Interactive System Design. Course by Chris Johnson, 1993
[1] Course by Prof. Birgit Weller, 2020

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