Feature Engineering at Scale
In this course, you will gain a comprehensive understanding of how to design, scale, and operationalize end-to-end feature engineering pipelines on the Databricks platform. The curriculum is structured across three progressive modules: mastering the fundamentals of Spark’s distributed execution and optimization, implementing scalable data ingestion with Auto Loader and declarative Lakeflow pipelines, and advancing to production-grade MLOps with the Databricks Feature Store.
You will engage in hands-on learning experiences such as debugging Spark performance with the Catalyst Optimizer and Spark UI, building robust Bronze-Silver-Gold medallion architectures with automated quality checks, and implementing scalable feature transformations using SparkML. The course culminates in deploying real-time feature serving through Online Feature Stores, defining FeatureSpecs with on-demand transformations, and applying governance and lineage tracking with Unity Catalog.
Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.
The content was developed for participants with these skills/knowledge/abilities:
1. Completed the “Introduction to Apache Spark” course or possess equivalent foundational knowledge of Spark, including basic data transformations and Spark SQL.
* Learners should be comfortable with Spark’s role in distributed data processing. This course will build on that foundation to explain how Spark enables scalable machine learning workflows.
2. Intermediate-level proficiency in Python programming, particularly for data manipulation using libraries such as `pandas`, `numpy`, or `scikit-learn`.
3. Intermediate understanding of traditional machine learning workflows, including model training, evaluation, and hyperparameter tuning.
4. Familiarity with the Databricks platform and workflows.
* Learners are strongly encouraged to complete the Databricks Machine Learning Associate course prior to this course. This course assumes knowledge of ML development using the Databricks environment.
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details
