Data Engineer to MLOps: From Data Pipelines to ML Pipelines

Your data engineering expertise is the foundation MLOps is built on. As a data engineer, you already understand the hardest parts of ML systems, reliable data pipelines, orchestration, data quality, and production infrastructure. The transition to MLOps is about extending these skills to handle the unique challenges of machine learning workflows. Your experience with ETL processes translates directly to feature engineering pipelines. Your Airflow or Prefect knowledge applies to ML workflow orchestration. Your understanding of data versioning and lineage is critical for experiment tracking and model reproducibility. What makes this transition particularly natural is that 80% of ML system failures come from data issues, not model issues. You already have the mindset to build robust, monitored, production-grade systems. The new skills you'll add (feature stores, model serving, experiment tracking, and ML-specific monitoring) build on patterns you already know. You'll learn to think about data not just as something to move and transform, but as the fuel for models that need consistent, versioned, and validated features. This path takes 3-5 months because you're not starting from scratch, you're specializing. By the end, you'll understand the full ML lifecycle from feature engineering through model deployment and monitoring, with the production engineering rigor that separates hobby projects from enterprise ML systems.