Get Started with Lakehouse Architecture on Databricks

In this course, you will develop the foundational skills needed to use the Databricks Data Intelligence Platform for executing basic machine learning workflows and supporting data science workloads. You will explore the platform from the perspective of a machine learning practitioner, covering topics such as feature engineering with Databricks Notebooks and model lifecycle tracking with MLflow. Additionally, you will learn about real-time model inference with Mosaic AI Model Serving and experience Databricks’ “glass box” approach to model development through AutoML.

Languages Available: English | 日本語 | Português BR | 한국어

In this course, you will learn how to self-serve business insights from your company’s Databricks Data Intelligence Platform using AI/BI. After a tour of the fundamental components of the platform, you’ll learn how to interact with pre-created AI/BI Dashboards to explore your company’s data through existing charts and visualizations. You’ll also learn how to use AI/BI Genie to go beyond dashboards by asking follow-up questions in natural language to self-serve new insights, create visualizations, and share them with your colleagues.

The course also includes two guided demonstrations for hands-on practice in a virtual environment.

This course provides a comprehensive understanding of Spark Structured Streaming and Delta Lake, including computation models, configuration for streaming read, and maintaining data quality in a streaming environment.

Note: This course is part of the 'Advanced Data Engineering with Databricks' course series.