AI Agent Fundamentals
This foundational course introduces AI agents and their use in enterprise applications on Databricks, including the Mosaic AI platform and Agent Bricks. Learners will examine what AI agents are, how they function, and how they mimic human reasoning to handle complex tasks.
The course covers real-world agent use cases and provides a basic introduction to advanced topics such as agentic workflows and multi-agent systems. It also explores how Agent Bricks simplifies the development of enterprise-ready agents across various applications, with demos showing how to build and use agents on Databricks.
Note: 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.
Languages Available: English | 日本語 | Português BR | 한국어
The content was developed for participants with these skills/knowledge/abilities:
• Completed the Get Started with Databricks for Machine Learning (Onboarding) course or possess equivalent foundational experience with the Databricks platform.
- Learners should be familiar with basic Databricks workspace operations, including navigating the UI, creating notebooks, and accessing common workspace features.
• Basic understanding of artificial intelligence and machine learning fundamentals.
- This includes introductory knowledge of large language models (LLMs), their capabilities, and common use cases.
• Beginner-level familiarity with prompt engineering and natural language interaction with AI models.
- Learners should have experience writing simple prompts and understanding how AI models respond to natural language inputs.
• Introductory knowledge of Unity Catalog concepts for governance and asset management.
- Learners should be aware of how data and AI assets are organized, secured, and governed within the Databricks platform.
• Basic understanding of document processing and information extraction concepts.
- This includes familiarity with different file formats, data types, and how unstructured data can be processed for downstream AI applications.
• Familiarity with no-code and low-code development approaches.
- Learners should understand when no-code or low-code tools are appropriate for rapidly building and prototyping AI solutions.
• Introductory awareness of AI agent concepts and workflows.
- Learners should understand the distinction between structured and unstructured data processing tasks and how AI agents can orchestrate multi-step workflows.
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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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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Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details
