machine-learning-engineer

Production ML pipelines require orchestrating end-to-end workflows from data ingestion to deployment with experiment tracking, versioning, and governance guardrails to ensure reproducibility and auditability across teams.

Yes, the ML workflow stack supports both PyTorch transformers and scikit-learn models. It applies modular, pluggable components for prototyping, experimentation, and drift monitoring across both classical ML and deep learning frameworks.

You track machine learning experiments and manage model versioning by applying MLflow tracking and DVC data version control within the workflow, ensuring reproducible, auditable pipelines and governance guardrails throughout the model lifecycle.

The best way to detect data drift in deployed models is to orchestrate end-to-end ML workflows with built-in drift monitoring capabilities, applying modular preprocessing and evaluation stacks for continuous assessment.

Yes, you can use MLflow and PyTorch together for MLOps. The stack orchestrates end-to-end workflows with MLflow tracking, REST serving patterns, and PyTorch dependencies to deliver production-grade deep learning models.