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Home Specialist skills Artificial Intelligence Gen AI Engineering with Databricks

Gen AI Engineering with Databricks

Implement retrieval-augmented generation (RAG) solutions using Databricks
Build and evaluate generative AI applications with multi-stage reasoning LLM chains
Apply governance and evaluation techniques to generative AI models
Deploy and monitor generative AI applications using Model Serving and Lakehouse Monitoring
Design autonomous AI agents with Databricks-based cognitive architectures.

Overview

Off the shelf (OTS)

This course is designed for data scientists, machine learning engineers, and data practitioners who want to build generative AI applications using Databricks. It provides hands-on experience with the latest frameworks and Databricks capabilities to develop, evaluate, and deploy generative AI solutions.

Participants will gain expertise in retrieval-augmented generation (RAG), multi-stage reasoning LLM chains, AI application governance, and model deployment. Through practical exercises and real-world use cases, learners will develop end-to-end generative AI applications while ensuring compliance and performance monitoring.

Participants should have:
• Familiarity with natural language processing concepts.
• Understanding of prompt engineering and best practices.
• Experience with the Databricks Data Intelligence Platform.
• Knowledge of RAG concepts, including data preparation, embedding, vectors, and vector databases.
• Experience in building LLM applications using multi-stage reasoning LLM chains and agents.
• Familiarity with Databricks tools for AI evaluation and governance.

This course is intended for:
• Data scientists and machine learning engineers developing AI-driven applications.
• AI practitioners looking to enhance their skills in generative AI with Databricks.
• Organisations seeking to deploy and govern large-scale AI applications effectively.

This course includes:
• Practical exercises with Databricks for generative AI model development.
• Hands-on labs covering RAG architecture, multi-stage reasoning, and agent design.
• Real-world AI governance and evaluation case studies.
• Guided model deployment and monitoring activities using Databricks tools.

This course is not specifically aligned with an exam.