Technical Mock Interview: Machine Learning Pipeline Design

Let’s explore two interview questions that are typically asked on phone/web screens at top companies (e.g., FAANG) as part of machine learning system design interviews. Each question will challenge you to think about model architecture design and explain how you would select an appropriate model and align it with specific business requirements. You can also implement a small Python code snippet.

It will take about 15-20 minutes for an interviewee to tackle each of the following questions.

Advanced system design

In the context of a machine learning system design interview, can you show me how you would design a model architecture for a recommendation system? Explain the steps you would take to select and justify the model architecture. Feel free to implement a simple pseudocode snippet to show me how you would implement your approach.

Sample answer

You can approach this question in several ways. Let's consider a sample approach:

I would suggest implementing a collaborative filtering recommendation model using matrix factorization for this use case.

Justifying steps

1. Understand business requirements: I would start by deeply engaging with stakeholders to establish the business goals and success metrics for the recommendation system. Beyond traditional KPIs like user engagement, click-through rate, and conversion rate, I’d consider metrics that capture recommendation quality such as user satisfaction, discovery rate, and diversity of recommendations. These insights would shape the system architecture, ensuring it meets the broader strategic goals of enhancing user experience and driving business growth.

2. Explore the data: My data exploration process would cover multiple dimensions and data sources, including:

   1. User-item interaction data: It includes both explicit feedback (e.g., ratings) and implicit feedback (e.g., clicks, watch time).

   2. User demographics: It includes detailed attributes like age, location, and preferences.

   3. Item metadata: It includes features such as categories, tags, descriptions, and even multimedia content.

   4. Contextual signals: It includes time of day, device type, session duration, etc.

   5. I’d also identify data sparsity or quality issues early, which would inform decisions like how to handle cold-start scenarios for new users or items.

3. Design a hybrid model: To address modern requirements, I’d go ...