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

Systems design has appeared in a variety of fields, including sustainability, computer/software architecture, and sociology.

Product Development 
If the broader topic of product development "blends the perspective of marketing, design, and manufacturing into a single approach to product development," 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.

Physical design 
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.

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 design
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 Design
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.
 Data Pipeline: Build automated pipelines to collect, clean, transform, and validate data.
 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).
 Monitoring and Maintenance: Continuously monitor model performance, retrain as necessary, and ensure data drift is addressed.

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 

 Arcadia (engineering)
 Architectural pattern (computer science)
 Configuration design
 Electronic design automation (EDA)
 Electronic system-level (ESL)
 Embedded system
 Graphical system design
 Hypersystems
 Modular design
 Morphological analysis (problem-solving)
 Systems analysis and design
 SCSD (School Construction Systems Development) project
 System information modelling
 System development life cycle (SDLC)
 System engineering
 System thinking
 TRIZ

References

Further reading

External links 

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

Computer systems
Electronic design automation
Software design