Data Engineering and MLOps play complementary roles in the machine learning lifecycle but focus on different responsibilities and skill sets. Data Engineers are responsible for building and maintaining data infrastructure, including designing data pipelines, managing data storage systems, performing ETL/ELT processes, and ensuring that data is clean, reliable, and accessible for analytics and machine learning. Their work typically involves tools and technologies such as Spark, Kafka, Hadoop, SQL databases, and cloud data platforms, along with strong skills in data modeling and distributed systems. MLOps, on the other hand, focuses on operationalizing machine learning models—automating model training, versioning datasets and models, deploying models into production, monitoring model performance, and handling model drift or retraining. MLOps engineers often work with tools like MLflow, Kubeflow, Docker, Kubernetes, and CI/CD pipelines, requiring knowledge of machine learning workflows, automation, and cloud infrastructure. In real-world AI projects, Data Engineering ensures high-quality data pipelines that feed machine learning systems, while MLOps ensures that trained models are reliably deployed, monitored, and continuously improved in production. Together, these roles enable organizations to deliver scalable, reliable AI-driven solutions that turn raw data into actionable intelligence and business value.