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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
4h
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

See all our registration options

Registration options

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

Runtime

Self-Paced

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

Register now

Instructors

Instructor-Led

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

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Learning

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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Scale

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 Declarative Automation 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 Declarative Automation 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 Declarative Automation 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 Declarative Automation 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 Declarative Automation Bundles.

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

Note: 

1. 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!

2. This course is the fourth in the 'Advanced Data Engineering with Databricks' series.

Languages Available: English | 日本語 | 한국어

Free
2h
Professional

Questions?

If you have any questions, please refer to our Frequently Asked Questions page.