MLOps Engineer interview questions

An MLOps Engineer is responsible for integrating machine learning models into production systems, automating machine learning workflows, and ensuring models are scalable and maintainable.

MLOps involves continuous integration and deployment of machine learning models, includes data validation, model monitoring, and retraining. Compared to traditional DevOps, it has additional complexities due to data dependencies and model versioning.

Common MLOps tools include Kubernetes for orchestration, TensorFlow Extended (TFX), MLflow for tracking experiments, Kubeflow for pipelines, and Docker for containerization.

Version control is crucial for tracking changes in data, code, and models. It ensures reproducibility, collaboration, and accountability in the machine learning lifecycle.

A deployment pipeline automates the process of getting a machine learning model from development into production. This includes stages like testing, integration, deployment, and monitoring.

Monitoring involves tracking metrics such as accuracy, data drift, latency, and resource usage. Tools like Prometheus, Grafana, and custom logging solutions are often used.

CI/CD in MLOps involves regularly integrating model changes and automatically deploying them to production systems, facilitating faster iteration and real-time updates.

Data versioning can be managed through tools like DVC or Delta Lake, which help track changes, manage datasets, and ensure consistency across different stages of the ML lifecycle.

Infrastructure as code allows for the automated provisioning and management of cloud and on-premise resources, ensuring that environments are consistent and scalable.

One challenge is managing model drift, which can be addressed by implementing automated monitoring to detect performance degradation and setting up retraining pipelines to update models as needed.