In the modern enterprise, the divide between data engineering and data science is often a primary bottleneck for innovation. Data engineers live in the world of distributed clusters, Spark, and ETL pipelines, while data scientists thrive in experimental environments, model tracking, and hyperparameter tuning.
Azure provides two powerhouse platforms to address these needs: Azure Databricks and Azure Machine Learning (Azure ML). While they share some overlapping features, their true potential is unlocked when integrated into a single, cohesive ecosystem. This article provides a deep-dive into why and how you should combine these technologies to build a production-grade Big Data ML pipeline.