Fundamentals of generative AI and large language models
- Overview of generative AI and its role in modern AI platforms
- Understanding large language models and transformer architectures
- Common use cases for enterprise generative AI solutions
- Key challenges in deploying generative AI systems at scale
Using Azure Databricks for generative AI workloads
- Introduction to Azure Databricks as a unified analytics platform
- Leveraging Apache Spark for distributed AI workloads
- Managing data pipelines for generative AI applications
- Integrating Databricks with Azure AI services
Retrieval-augmented generation architectures
- Principles of retrieval-augmented generation
- Combining vector search with language models
- Designing pipelines for contextual data retrieval
- Improving response accuracy and relevance with external knowledge sources
Multi-stage and agent-style reasoning patterns
- Understanding multi-step reasoning in generative AI systems
- Designing agent-based workflows using large language models
- Orchestrating tools and APIs within AI pipelines
- Enhancing decision-making through chained reasoning approaches
Fine-tuning large language models
- Overview of fine-tuning techniques and approaches
- Preparing datasets for supervised fine-tuning
- Parameter-efficient tuning methods
- Evaluating improvements from fine-tuned models
Evaluating generative AI systems
- Key evaluation metrics for large language models
- Automated and human-in-the-loop evaluation strategies
- Detecting bias, hallucinations, and model drift
- Benchmarking and continuous improvement practices
Responsible AI and governance considerations
- Principles of responsible AI in generative systems
- Managing risk, bias, and ethical concerns
- Ensuring compliance with organisational and regulatory standards
- Implementing governance frameworks for AI solutions
Managing generative AI solutions with LLMOps
- Introduction to large language model operations
- Versioning, monitoring, and lifecycle management of models
- Deploying generative AI applications in production
- Scaling and maintaining AI systems using Azure Databricks
Exams and assessments
There are no formal exams included in this course. Learners will complete knowledge checks and practical exercises throughout the day to reinforce key concepts and validate understanding of generative AI engineering techniques.
Hands-on learning
This course includes:
- Guided labs using Azure Databricks for generative AI workflows
- Practical exercises in retrieval-augmented generation and model fine-tuning
- Scenario-based activities focused on real-world AI engineering challenges
- Instructor-led discussions on applying LLMOps in production environments
