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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:

• Familiarity with the Databricks Data Intelligence Platform, specifically Unity Catalog, Delta Lake, and MLflow capabilities,

• Foundational knowledge of Machine Learning and Generative AI concepts, including model training, fine-tuning, inference, and Large Language Models (LLMs),

• Understanding of Retrieval Augmented Generation (RAG) architectures and vector database concepts,

• Familiarity with the AI/ML software development lifecycle (SDLC) and MLOps principles, including ModelOps, DataOps, and DevSecOps,

• Basic familiarity with cloud security principles, such as Identity and Access Management (IAM), encryption, and network security controls,

• Understanding of data governance topics, including access control lists (ACLs), data lineage, and cataloging features,

• Basic knowledge of Python programming and SQL for data manipulation and querying within a notebook environment,

Self-Paced

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

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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

Data Ingestion with Lakeflow Connect - Mandarin Chinese

本课程全面介绍 LakeFlow Connect——一种可扩展且简便的解决方案,用于将来自各种来源的数据摄取到 Databricks 中。您将首先浏览 LakeFlow Connect 连接器的不同类型(标准型和托管型),并学习各种数据摄取技术,包括批处理摄取、增量批处理摄取和流式处理摄取。您还将了解使用 Delta 表和金银铜架构的主要优势。

接下来,您将培养使用 LakeFlow Connect 标准连接器从云对象存储摄取数据的实践技能。这包括使用 CREATE TABLE AS SELECT(CTAS)、COPY INTO 和 Auto Loader 等方法,并重点介绍每种方法的优势和注意事项。您还将学习如何在将数据摄取到 Databricks Data Intelligence Platform 的过程中,向铜层表追加元数据列。课程随后介绍如何使用救援数据列处理与表架构不匹配的记录,以及管理和分析此类数据的策略。您还将探索摄取和展平半结构化 JSON 数据的技术。

此后,您将探索如何使用 LakeFlow Connect 托管连接器执行企业级数据摄取,以引入来自数据库和软件即服务(SaaS)应用程序的数据。课程还将介绍 Partner Connect,作为将合作伙伴工具集成到摄取工作负载中的一种选项。

最后,课程以替代摄取策略作为总结,包括 MERGE INTO 运营以及利用 Databricks Marketplace,为您奠定坚实的基础,以支持现代数据工程用例。

注意:对于 SCORM 课程文件,请确保完成内容后关闭 SCORM 窗口。请勿点击“下一课”按钮,否则可能导致 SCORM 模块无法标记为已完成。

Free
2h
Associate

Questions?

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