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Databricks AI Security Fundamentals

In this course, you will explore the fundamentals of security in AI systems within the Databricks Data Intelligence Platform. The course covers five comprehensive modules and delves into the significance of securing AI systems and navigating compliance with evolving legal and regulatory standards. You will examine recent security incidents, identify various AI model types, and assess security risks across AI system components. Additionally, you will learn to leverage Databricks features for enhanced AI security, illustrating best practices for risk mitigation. By the end of the course, you will possess the knowledge and skills necessary to implement secure AI solutions and apply effective security measures in real-world scenarios.

Skill Level
Introductory
Duration
1h
Prerequisites

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

  • Basic knowledge of cloud computing and data governance techniques.

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

Build Data Pipelines with Lakeflow Declarative Pipelines

This course introduces users to the essential concepts and skills needed to build data pipelines using Lakeflow Declarative Pipelines in Databricks for incremental batch or streaming ingestion and processing through multiple streaming tables and materialized views. Designed for data engineers new to Lakeflow Declarative Pipelines, the course provides a comprehensive overview of core components such as incremental data processing, streaming tables, materialized views, and temporary views, highlighting their specific purposes and differences.

Topics covered include:

- Developing and debugging ETL pipelines with the multi-file editor in Lakeflow using SQL (with Python code examples provided)

- How Lakeflow Declarative Pipelines track data dependencies in a pipeline through the pipeline graph

- Configuring pipeline compute resources, data assets, trigger modes, and other advanced options

Next, the course introduces data quality expectations in Lakeflow, guiding users through the process of integrating expectations into pipelines to validate and enforce data integrity. Learners will then explore how to put a pipeline into production, including scheduling options, and enabling pipeline event logging to monitor pipeline performance and health.

Finally, the course covers how to implement Change Data Capture (CDC) using the AUTO CDC INTO syntax within Lakeflow Declarative Pipelines to manage slowly changing dimensions (SCD Type 1 and Type 2), preparing users to integrate CDC into their own pipelines.

Note: 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!

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

Free
2h
Associate

Questions?

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