: Design how the model will serve predictions—either via online inference (low latency) or batch processing .
Several repositories have become the gold standard for ML system design prep, often containing direct links to downloadable : ml-system-design.md - Machine-Learning-Interviews - GitHub Machine Learning System Design Interview Pdf Github
A consistent, flexible framework is essential for navigating the complexities of an ML design session. Top GitHub repositories often cite a version of this 9-step "formula": : Design how the model will serve predictions—either
: Select and represent features (e.g., embeddings for images or text). : Define the business goal and use cases
: Define the business goal and use cases. Clarify whether an ML solution is even necessary or if a rule-based system suffices.
: Outline the high-level MVP logic, deciding between simple baseline models and complex architectures.
: Plan for A/B testing, shadow deployments, and canary releases.