Get Started with Databricks for Machine Learning
Contact us to book this course
Machine Learning and AI
On-Site, Virtual
1/2 day
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. The course includes three instructor-led demonstrations, culminating in a comprehensive lab that reinforces the concepts covered in the demos.
Objectives
By the end of this course, you will be able to:
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Describe fundamental concepts for ML using the Databricks Data Intelligence Platform.
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Perform basic notebook tasks using the Databricks Data Intelligence Platform.
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Store and manage data for ML tasks.
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Describe features available within Databricks for end-to-end machine learning development.
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Create and use a baseline model using AutoML.
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Create and use a feature store table for model training.
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Use MLflow to track, stage, and manage the model lifecycle.
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Use a registered model for real-time inference with Mosaic AI Model Serving.
Prerequisites
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A beginner-level understanding of Python.
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Basic understanding of DS/ML concepts (e.g. classification and regression models), common model metrics (e.g. F1-score), and Python libraries (e.g. scikit-learn and XGBoost).
Course outline
- Databricks Infrastructure
- Databricks Data Intelligence Platform
- Unity Catalog Overview
- Databricks Workspace Walkthrough
- Introduction to Machine Learning on Databricks
- Exploratory Data Analysis (EDA) and Feature Engineering on Databricks
- Introduction to Mosaic AI AutoML
- Introduction to MLflow on Databricks
- Introduction to Mosaic AI Model Serving
