Machine Learning at Scale

In this course, you will gain theoretical and practical knowledge of Apache Spark’s architecture and its application to machine learning workloads within Databricks. You will learn when to use Spark for data preparation, model training, and deployment, while also gaining hands-on experience with Spark ML and pandas APIs on Spark. This course will introduce you to advanced concepts like hyperparameter tuning and scaling Optuna with Spark. This course will use features and concepts introduced in the associate course, such as MLflow and Unity Catalog, for comprehensive model packaging and governance.

Note: This course is the first in the series of Advanced Machine Learning.

Skill Level

Professional

Duration

Prerequisites

The content was developed for participants with these skills/knowledge/abilities:

A beginner-level understanding of Python.
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).

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

See all our registration options

Registration options

Databricks has a delivery method for wherever you are on your learning journey

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

Instructor-Led

Public and private courses taught by expert instructors across half-day to two-day courses

Blended Learning

Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase

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Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Data Engineer

Automated Deployment with Databricks Asset Bundles

This course provides a comprehensive review of DevOps principles and their application to Databricks projects. It begins with an overview of core DevOps, DataOps, continuous integration (CI), continuous deployment (CD), and testing, and explores how these principles can be applied to data engineering pipelines.

The course then focuses on continuous deployment within the CI/CD process, examining tools like the Databricks REST API, SDK, and CLI for project deployment. You will learn about Databricks Asset Bundles (DABs) and how they fit into the CI/CD process. You’ll dive into their key components, folder structure, and how they streamline deployment across various target environments in Databricks. You will also learn how to add variables, modify, validate, deploy, and execute Databricks Asset Bundles for multiple environments with different configurations using the Databricks CLI.

Finally, the course introduces Visual Studio Code as an Interactive Development Environment (IDE) for building, testing, and deploying Databricks Asset Bundles locally, optimizing your development process. The course concludes with an introduction to automating deployment pipelines using GitHub Actions to enhance the CI/CD workflow with Databricks Asset Bundles.

By the end of this course, you will be equipped to automate Databricks project deployments with Databricks Asset Bundles, improving efficiency through DevOps practices.

Free

Professional

Data Analyst

AI/BI for Data Analysts

In this course, you’ll learn how to use the features Databricks provides for business intelligence needs: AI/BI Dashboards and AI/BI Genie. As a Databricks Data Analyst, you will be tasked with creating AI/BI Dashboards and AI/BI Genie Spaces within the platform, managing the access to these assets by stakeholders and necessary parties, and maintaining these assets as they are edited, refreshed, or decommissioned over the course of their lifespan. This course intends to instruct participants on how to design dashboards for business insights, share those with collaborators and stakeholders, and maintain those assets within the platform. Participants will also learn how to utilize AI/BI Genie Spaces to support self-service analytics through the creation and maintenance of these environments powered by the Databricks Data Intelligence Engine.

Free

Associate

Create Your First Workspace Using Databricks Express

In this course, you will explore the core features and functionalities of Databricks Express Setup, a streamlined way to get started with Databricks. The course is designed to help you quickly set up and navigate a serverless workspace while providing a comprehensive understanding of the credit-based trial system, including the $400 allowance. Divided into six modules, it starts with an introduction to Databricks Express Setup, followed by an exploration of its key features and benefits. You will learn to create and manage serverless workspaces, perform exploratory data analysis using Unity Catalog, and gain insights into collaboration through data sharing.

Additionally, you’ll be guided through trial management and upgrade options, ensuring you can effectively transition from trial to paid accounts. The course also includes an internal-only module on the evolution of trial credits and account activation methods. By the end of the course, you will have a solid foundation in using Databricks Express Setup for data and AI workloads, enabling you to confidently explore and analyze data, collaborate with peers, and manage your Databricks environment efficiently.

Free

1h 30m

Introductory