Editing Systems design (section)

===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 [[Recommender system|recommendation engines]], [[Artificial intelligence in fraud detection|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>{{Cite book |last=Sorvisto |first=Dayne |title=MLOps Lifecycle Toolkit: A Software Engineering Roadmap for Designing, Deploying, and Scaling Stochastic Systems |publisher=Apress |year=2023 |isbn=978-1-4842-9641-7}}</ref>
# Data Pipeline: Build automated pipelines to collect, clean, transform, and validate data.<ref>{{Cite book |last=Polyzotis |first=Neoklis |chapter=Data Management Challenges in Production Machine Learning |date=2017 |pages=1723–1726 |title=Proceedings of the 2017 ACM International Conference on Management of Data |doi=10.1145/3035918.3054782|isbn=978-1-4503-4197-4 }}</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 (software)|Docker]] and [[Kubernetes]]).<ref>{{Cite book |last=Huyen |first=Chip |title=Designing Machine Learning Systems |publisher=O'Reilly Media |year=2022 |isbn=978-1-098-10796-3}}</ref>
# Monitoring and Maintenance: Continuously monitor model performance, retrain as necessary, and ensure [[Concept drift|data drift]] is addressed.<ref>{{Cite web |title=Machine Learning at Scale: Challenges and Best Practices |url=https://cloud.google.com/blog/topics/developers-practitioners/machine-learning-scale-challenges-and-best-practices |website=Google Cloud Blog |date=2020}}</ref>

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.