Fundamentals of MLOps

Step into a coding-focused interview experience designed to simulate a real-world technical screening for an MLOps or Machine Learning Engineering role. You’ll work through exercises covering data pipelines, model training, experiment tracking, deployment, monitoring, and production best practices—all via coding tasks rather than system design.

Throughout the interview, you’ll write scripts, manipulate data, train and validate models, track experiments programmatically, and implement monitoring checks. Expect to reason through trade-offs, debug workflows, and demonstrate practical coding proficiency while ensuring reproducibility and reliability.

Sharpen your skills in:

• Implementing scalable data pipelines in code
• Training, versioning, and validating ML models
• Programmatically tracking experiments and metrics
• Deploying and testing models in production-like scenarios
• Applying coding best practices and documenting results clearly

By the end, you’ll gain practical experience in coding-driven MLOps tasks and be prepared to solve real-world workflow problems efficiently.