ML System Design Interview

Building ML systems in production requires far more than training a model with high accuracy. In this session, we will break down the complete Machine Learning lifecycle—from business requirements to continuous monitoring in production.

What we can cover in this session:

• Problem Formulation & Metrics: Frame ambiguous business problems into ML tasks, and select the right online/offline metrics.
• Data & Feature Engineering: Design scalable data pipelines, feature stores, handling cold starts, and batch vs. streaming features.
• Model Selection & Training: Choose the right model architecture (Classical ML vs. Deep Learning vs. LLMs), baseline strategy, and distributed training setups.
• Deployment & Serving: Real-time inference vs. batch prediction, microservice integration, model quantization, and low-latency API design.
• Monitoring & Lifecycle: Track data drift, concept drift, feature attribution, logging, and automated retraining pipelines.

Who is this for?

Machine Learning Engineers, Data Scientists, or Software Engineers preparing for ML System Design interviews or scaling AI models in production.

Bring 1–2 specific problems or topics you want to target, or let me curate a set based on your goal!