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:

• Familiarity with the Databricks Data Intelligence Platform and basic workspace operations (create clusters, run code in notebooks, use basic notebook operations, import repos from git).

• Intermediate programming experience with Python, including data manipulation libraries (pandas, numpy) and machine learning frameworks (scikit-learn).

• Basic knowledge of Apache Spark and PySpark fundamentals, including DataFrames, transformations, and actions for distributed data processing.

• Understanding of machine learning concepts, including model training, evaluation, hyperparameter tuning, and deployment workflows.

• Intermediate experience with Delta Lake operations (create tables, perform updates, optimize files, time travel functionality).

• Basic familiarity with MLflow for experiment tracking, model logging, and model registry operations.

• Understanding of distributed computing concepts (cluster architecture, parallelization, scalability considerations).

• Basic knowledge of SQL for data querying and manipulation within Spark environments.

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

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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

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.

Paid & Subscription

Lab

Professional

Platform Administrator

Get Started with Data Governance on Databricks

In this course, you will explore how Unity Catalog enables secure, centralized data governance and fine-grained access control on Databricks. You will learn about table and volume types, catalog and schema configuration, group-based access management, and strategies for migrating existing access controls into Unity Catalog. The course also explains how to design and apply fine-grained controls such as row-level security, column masking, and attribute-based access control, how to combine these mechanisms across data and AI assets, and how to align them with broader governance requirements for compliant, scalable access management.

Languages Available: English | 日本語 | 한국어

Paid & Subscription

Lab

Onboarding

Data Warehousing Practitioner

Delivery Specialization: CDW Migration Best Practices

This course is meant to provide guidance on best practices for executing a successful migration from Cloud Data Warehouses (CDWs) - e.g. Amazon Redshift, Snowflake, and Synapse Analytics - to Databricks. It covers a wide range of migration processes starting from initial discovery all the way through to delivery.