Validates ability to use Databricks for the full ML lifecycle.
Answer : MLflow.
Handles experiment tracking, model packaging, versioning, and deployment orchestration.
Answer : An optimized storage layer that brings ACID transactions and reliability to Apache Spark.
Provides the high-performance data foundation necessary for reliable machine learning pipelines.
Answer : It automatically builds models and generates the underlying Python notebooks for review.
Empowers data scientists to quickly establish a performance baseline for new datasets.
Answer : By using the Databricks Feature Store.
Ensures that features are engineered once and applied consistently in both training and production.
Answer : Apache Spark.
The distributed computing framework designed for fast processing of large-scale datasets.