Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
AI Infra Engineering
·
Module 1
Distributed Systems Foundations
1
CAP Theorem: Trade-offs in System Design
Explain the CAP theorem and analyze its trade-offs in real-world system design decisions.
2
Strong vs. Eventual Consistency in AI Systems
Compare strong versus eventual consistency models using practical examples from AI systems.
3
Distributed Consensus: Raft & Paxos Explained
Describe how consensus algorithms like Raft or Paxos achieve agreement across distributed nodes.
4
Data Partitioning and Sharding for Stateful Services
Explain data partitioning and sharding strategies for horizontally scaling stateful services like model metadata stores.
5
Synchronous vs. Asynchronous Replication: Durability-Performance Trade-offs
Compare synchronous versus asynchronous replication and their durability-performance trade-offs.
6
Distributed Communication Patterns for AI Systems
Analyze distributed communication patterns (RPC, message queues, pub-sub) and select the appropriate pattern for a given AI system requirement.
7
Consistent Hashing for Scalable Load Distribution
Explain consistent hashing and its role in scalable load distribution.
Next module isn't ready yet,
Back to course
Previous module
Next module