ML Interview Questions

Explain BPE, WordPiece, and other subword tokenization methods and their advantages in Natural Language Processing.

How do you handle out-of-vocabulary (OOV) words in natural language processing systems, and what are some techniques to address this issue effectively?

Describe the evolution of sentiment analysis techniques from rule-based systems to deep learning methods, highlighting their theoretical foundations and practical applications.

Explain BERT's architecture, pretraining objectives, and fine-tuning process.

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.