Implement Generative AI engineering with Azure Databricks
- Duration: 1 day
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
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Course Overview
The Implement Generative AI Engineering with Azure Databricks Course delivers specialized training for professionals looking to engineer scalable generative AI solutions on Azure. Participants gain a structured understanding of how Databricks supports data preparation, model integration, and end-to-end AI workflows. The course emphasizes practical engineering patterns—enabling learners to move from raw data to production-ready generative AI applications that deliver meaningful business outcomes.
Course Details
- Build practical skills in engineering generative AI solutions with Azure Databricks.
- Understand LLM concepts and generative AI architectures.
- Learn how to prepare, manage, and optimize data for AI workloads.
- Apply prompt engineering and embedding techniques for AI applications.
- Gain insight into evaluation, monitoring, and responsible AI practices.
- Establish a foundation for advanced AI and data engineering roles.
- Ideal for data engineers, data scientists, and AI engineers.
- Suitable for professionals building analytics or AI-driven applications.
- Basic familiarity with data engineering and Python is beneficial.
- Prior exposure to cloud or analytics platforms is helpful but not mandatory.
- Explain generative AI concepts and LLM-based solution patterns.
- Prepare and manage data for generative AI workloads using Databricks.
- Implement prompt engineering and embedding-based retrieval techniques.
- Integrate generative AI capabilities into analytics workflows.
- Evaluate and monitor generative AI solutions effectively.
- Apply responsible AI and governance considerations in production scenarios.
- Generative AI Fundamentals: Concepts, use cases, and architectures.
- Azure Databricks for AI: Platform capabilities and workflows.
- Data Preparation: Cleaning, transformation, and feature readiness.
- Prompt Engineering: Techniques for effective LLM interaction.
- Embeddings and Vector Search: Retrieval-augmented generation concepts.
- Solution Orchestration: End-to-end generative AI pipelines.
- Evaluation and Monitoring: Quality, performance, and reliability.
- Responsible AI: Governance, safety, and ethical considerations.
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