Step into a comprehensive interview experience that mirrors the challenges of designing a real-time ad recommendation system for top tech companies. You’ll be guided through a structured, five-phase process, starting from defining business objectives and requirements, moving through data pipeline design, model architecture, training and evaluation, and finally, deployment. Each phase features adaptive, progressively challenging questions tailored to your experience, ensuring a dynamic and realistic scenario.

Throughout the session, you’ll engage with a variety of question types, from direct knowledge checks to open-ended curveballs and follow-ups that probe your reasoning. You’ll be asked to illustrate your ideas using diagrams, just as you would in a real interview. Expect to tackle system scalability, feature engineering, model selection, and deployment trade-offs, all while justifying your design decisions.

Get ready to sharpen your skills in:

• Defining business and technical requirements for ad recommendation systems
• Designing robust data pipelines and feature engineering workflows
• Architecting and evaluating scalable, real-time ML models
• Addressing deployment, monitoring, and ethical considerations

By the end, you’ll be better equipped to confidently design and defend a real-time ad recommendation system, demonstrating both depth and breadth in ML system design interviews.