Databricks helped streamline the entire machine learning lifecycle with its comprehensive MLOps platform built on the unified Lakehouse architecture. It simplifies data ingestion, feature engineering, model training, and hyperparameter tuning using Apache Spark and Delta Lake. MLflow integration provides experiment tracking, model versioning, and governance, while Model Registry enables fast, reliable deployment. Automated monitoring detects data drift and performance degradation, triggering retraining workflows. This accelerates time-to-market, enhances collaboration, ensures scalability, and reduces operational overhead across ML development and production.

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