Model Development at Scale

In this course, you will develop an in-depth understanding of how to design, implement, and govern scalable machine learning systems that operate effectively at enterprise scale. The curriculum is organized into three experiential modules: developing distributed ML workflows with frameworks such as Apache SparkML and Ray, transitioning local ML development to distributed compute using tools like Pandas on Spark, and operationalizing and governing production models with Databricks’ MLOps ecosystem.

Through hands-on projects, you will construct end-to-end distributed ML pipelines using the SparkML workflow, applying Transformers, Estimators, and the fit/transform paradigm for both classification and regression tasks. You will version, compare, and manage experiments using MLflow 3.0 to ensure reproducibility and governance, capturing lineage between data, features, and model artifacts. Additionally, you will apply scalable Hyperparameter Optimization frameworks to improve model performance at scale.

The course concludes by demonstrating complete lifecycle management, from experimentation to production deployment, using Unity Catalog and Model Serving. You will learn to operationalize trained models, monitor their performance, and implement strong governance over models, features, and Delta assets within the Databricks environment.

Skill Level

Professional

Duration

Prerequisites

This course is best suited for learners with the following background:

⇾ Intermediate-level knowledge of traditional machine learning concepts.

⇾ Intermediate-level experience with traditional machine learning development on Databricks.

⇾ Intermediate-level knowledge of Python for machine learning projects.

⇾ Recommended: Intermediate-level experience with basic Spark concepts.

Self-Paced

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

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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

Data Ingestion with Lakeflow Connect

This course provides a comprehensive introduction to Lakeflow Connect, a scalable and simplified solution for ingesting data into Databricks from a wide range of sources. You’ll begin by exploring the different types of Lakeflow Connect connectors (Standard and Managed) and learn various data ingestion techniques, including batch, incremental batch, and streaming ingestion. You'll also review the key benefits of using Delta table and the Medallion architecture

Next, you’ll develop practical skills for ingesting data from cloud object storage using Lakeflow Connect Standard Connectors. This includes working with methods such as CREATE TABLE AS SELECT (CTAS), COPY INTO, and Auto Loader, with an emphasis on the benefits and considerations of each approach. You’ll also learn how to append metadata columns to your bronze-level tables during ingestion into the Databricks Data Intelligence Platform. The course then covers how to handle records that don’t match your table schema using the rescued data column, along with strategies for managing and analyzing this data. You’ll also explore techniques for ingesting and flattening semi-structured JSON data.

Following this, you’ll explore how to perform enterprise-grade data ingestion using Lakeflow Connect Managed Connectors to bring in data from databases and Software-as-a-Service (SaaS) applications. The course also introduces Partner Connect as an option for integrating partner tools into your ingestion workloads.

Finally, the course wraps up with alternative ingestion strategies, including MERGE INTO operations and leveraging the Databricks Marketplace, equipping you with a strong foundation to support modern data engineering use cases.

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.

Free

Associate

Generative AI Engineer

Generative AI Application Deployment and Monitoring

After consuming the content inside of this learning pathway, you should be able to deploy, operationalize, and monitor generative deploying, operationalizing, and monitoring generative AI applications. This content will help you gain skills in the deployment of generative AI applications using tools like Model Serving. We’ll also cover how to operationalize generative AI applications following best practices and recommended architectures. Finally, we’ll discuss the idea of monitoring generative AI applications and their components using Lakehouse Monitoring.

Note:

1. This is the fourth course in the 'Generative AI Engineering with Databricks’ series.

2. Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

Free

Associate

Answering Business Questions with Databricks Genie

This course introduces Databricks Genie, a conversational analytics tool that lets you ask plain-language questions of your organization's data and get governed, secure answers. You'll learn what a Genie Space is, how it keeps your data secure, and best practices for getting the best results. The course closes with a hands-on game where you will compete to investigate a business mystery.

Free

Onboarding