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Elite Computer Science Mastery
·
Module 11
Machine Learning Systems and MLOps
1
Data and Model Parallelism
Explain the concepts of data parallelism and model parallelism in distributed model training.
2
Feature Store Architecture for Skew Prevention & Scalability
Design the architecture of a feature store to prevent training-serving skew and serve features at scale.
3
Scalable Model Inference Architectures
Design scalable architectures for both online (real-time) and batch (offline) model inference.
4
Detecting Model Degradation: Data Drift, Concept Drift, and Monitoring Systems
Explain data drift and concept drift, and design a monitoring system to detect model degradation.
5
A/B Testing and Canary Deployments for ML Models
Implement A/B testing and canary deployment strategies for comparing ML models in production.
6
Model Optimization for Efficient Deployment
Apply model optimization techniques like quantization and knowledge distillation for efficient deployment.
7
Two-Stage Recommender System Architecture
Design the high-level architecture of a two-stage recommendation system (candidate generation and ranking).
8
Scalable Vector Database Architecture with ANN Indexing
Design the architecture of a scalable vector database using Approximate Nearest Neighbor (ANN) indexing.
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Scalable Data Processing and Platforms
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Interview Readiness and Practical Application