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Artificial Intelligence & Machine Learning
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Grokking the Machine Learning System Design Interview
Educative
Platform
· Washington, USA
About this course
Gain insights into designing robust machine learning systems using a structured 6-step framework. This course covers problem formulation, data strategy, model architecture, evaluation, deployment, and MLOps, with real-world case studies such as recommendation, fraud detection, semantic search, content moderation, and LLM-powered applications. It is designed to help senior engineers and ML practitioners ace machine learning system design interviews and build scalable, production-ready ML solutions aligned with business goals.
Prerequisite Skills
Skills you should have before taking this course
Python
probability and statistics
machine learning fundamentals
data structures and algorithms
Distributed systems basics
Skills Gained
Skills you will develop by completing this course
machine learning system design
recommendation systems
MLOps
Data pipelines
Model architecture design
Evaluation metrics and A/B testing
Fraud detection systems
Semantic search and retrieval-augmented generation (RAG)