ML Interview Questions

What are word embeddings, and how do models like Word2Vec and GloVe generate these embeddings? Discuss their differences and potential use cases in Natural Language Processing (NLP).

Explain the sequence-to-sequence (seq2seq) model and discuss its structure, working mechanism, and possible applications in NLP.

In the context of MLOps, explain how you would design a system to manage and version machine learning models. Discuss the role of a model registry, the importance of version control, and the challenges that might arise in maintaining and updating model artifacts.

Describe how you would design a system to monitor machine learning models in production, with a focus on detecting both data drift and concept drift. What tools and techniques would you employ, and how would you integrate them into an MLOps pipeline?

Explain the principles and practices of MLOps for managing the machine learning lifecycle, including how it integrates with existing DevOps practices.

Describe the components of an ML pipeline, from data ingestion to model serving, and explain the role of each component.

Design a comprehensive A/B test for a new feature in a machine learning system. Explain the steps you would take to ensure that the test is both statistically sound and practically applicable. Consider aspects such as sample size, duration, metrics to measure, and potential pitfalls.

Outline the architecture for an efficient image search system.

Design a scalable recommendation system for a large e-commerce platform. Discuss the architecture, key components, and how you would ensure it can handle millions of users and items. Consider both real-time and batch processing requirements.

Describe the different strategies for deploying machine learning models to production. Discuss the differences between batch processing and real-time processing in the context of ML model deployment. What are the considerations and trade-offs involved in choosing one over the other?