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Machine Learning System Design
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Module 1
Foundations of Distributed Systems for ML
1
Scalability Essentials: Vertical, Horizontal, Load Balancing, and Data Partitioning
Explain core scalability concepts: vertical/horizontal scaling, load balancing, and data partitioning.
2
Architectural Styles: Monoliths, Microservices, and Decoupling
Compare monolithic vs. microservice architectures and the role of REST APIs and message queues in decoupling components.
3
Data Storage Strategies for Machine Learning
Select appropriate storage solutions by comparing SQL, NoSQL, and caching strategies for different ML access patterns.
4
Understanding CAP Theorem Trade-offs
Describe the trade-offs in distributed systems using the CAP theorem.
5
Containerization & Orchestration for ML
Explain the purpose of containerization (Docker) and orchestration (Kubernetes) in deploying ML applications.
6
Back-of-the-Envelope System Estimation
Perform back-of-the-envelope estimations for system compute, storage, and network requirements.
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Data Management and Governance in ML