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

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 Interoperability with Unity Catalog

This course explores how to achieve seamless data interoperability between systems using Databricks Unity Catalog as a unified governance layer, using both Delta and Iceberg open table formats. Participants will learn to implement, manage and optimize a data environment where tables can be accessed by both Delta and Iceberg clients, enabling cross platform analytics and eliminating data silos.

Advanced Techniques with Spark Declarative Pipelines

This course explores Databricks' Lakeflow Spark Declarative Pipelines (SDP) for building production-grade streaming pipelines. You will learn advanced design patterns, robust data quality enforcement, and cross-platform integration essential for real-world lakehouse engineering.

Throughout the course, you will dive into modern data ingestion and processing techniques, mastering tools like Liquid Clustering for layout optimization and the Multiplex Streaming pattern for mixed-schema events. By the end of the modules, you will know how to confidently handle schema evolution, automate Change Data Capture (CDC), and ensure data integrity.

Through lectures and hands-on demos, you will:

• Build multi-flow pipelines to ingest multi-source data into a unified Bronze table.

• Apply Liquid Clustering and Data Quality Expectations across Silver and Gold layers.

• Implement the Multiplex pattern with Iceberg UniForm for cross-platform data access.

• Automate SCD Type 2 history tracking using AUTO CDC INTO.

• Design zero-data-loss quarantine pipelines to audit and manage invalid records.

Note:

1. This course is the first in the 'Advanced Data Engineering with Databricks' series.

2. 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.

Data Modeling Strategies

This course offers a deep dive into designing data models within the Databricks Lakehouse environment, and understanding the data products lifecycle. Participants will learn to align business requirements with data organization and model design leveraging Delta Lake and Unity Catalog for defining data architectures, and techniques for data integration and sharing.

Note: The course includes practice labs that the learners should perform after going through the entire course.

Languages Available: English | 日本語 | Português BR | 한국어

Paid & Subscription

Lab

Associate