Generative AI Training Overview
This Generative AI Engineering training course teaches attendees how to integrate LLMs (Large Language Models) into their AI applications. In addition, participants learn how to ensure their applications are secure and private.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, we offer some courses as live, instructor-led online classes for individuals.
Objectives
- Understand the importance and benefits of LLMs in software engineering and data engineering
- Identify suitable tools for LLM integration
- Apply techniques for fine-tuning
- Enhance chatbot performance with LLMs
- Ensure security and privacy
- Discuss ethical considerations
Prerequisites
All students must have extensive Python development experience and understand Core Data and Cloud concepts.
Outline
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Introduction
- Definition of LLMs (Large Language Models)
- Importance of LLMs in software engineering and data engineering
- Overview of the application of LLMs in software tools, data pipelines, and chatbots
Fine-tuning LLMs for Software and Data Engineering Tasks
- Understanding the training process
- Selecting appropriate datasets
- Domain-specific data (software engineering, data engineering)
- Data quality and diversity
- Techniques for fine-tuning
- Transfer learning
- Adaptive learning rates
- Regularization techniques
- Evaluating model performance
- Metrics for evaluation
- Identifying overfitting and underfitting
Integration of LLMs into Existing Software and Data Engineering Tools
- Identifying suitable tools for LLM integration
- Integrated development environments (IDEs)
- Data processing and analysis tools
- Workflow automation platforms
- Adapting LLMs to specific tasks
- Task-specific fine-tuning
- Interface design
- Ensuring seamless interaction
- API integration
- Data flow management
Integrating LLMs into Chatbot Applications for Software and Data Engineering Use Cases
- Chatbot architectures
- Retrieval-based chatbots
- Generative chatbots
- Enhancing chatbot performance with LLMs
- Natural language understanding
- Natural language generation
- Context-aware conversation handling
- Personalization and customization
- User profiling
- Adaptation to user preferences
- Ensuring security and privacy
- Data protection
- Anonymization techniques
Case Studies and Real-World Applications in Software and Data Engineering
- Examples of LLM integration in existing tools
- Successful chatbot applications using LLMs in software and data engineering contexts
- Lessons learned from practical applications
Future Perspectives and Challenges for Software and Data Engineers
- The evolving landscape of LLMs
- Ethical considerations
- Potential limitations and mitigation strategies
Conclusion
- Recap of the importance and benefits of LLMs in software engineering and data engineering
- The future of LLMs in software tools, data pipelines, and chatbot applications
Training Materials
All Generative AI training students receive comprehensive courseware.
Software Requirements
All attendees must have a modern web browser and an Internet connection.
