Course Code: generativeainllm
Duration: 14 hours
Prerequisites:
  • Basic understanding of machine learning and natural language processing concepts
  • Familiarity with Python programming

Audience

  • Data scientists and AI practitioners interested in generative AI technologies
  • Business professionals exploring automation and content generation
  • Technical managers and decision-makers looking to implement LLMs in their workflows
Overview:

Generative AI, driven by Large Language Models (LLMs) such as GPT and BERT, has transformed how businesses can automate tasks, generate content, and enhance customer service. These models use advanced transformer architectures to generate human-like text, automate communication, and provide personalized interactions, opening new possibilities across industries.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI professionals, business analysts, and technology leaders who wish to understand the principles of generative AI and the applications of LLMs in business settings. Participants will learn about transformers, prompt engineering, and ethical considerations in deploying these models for real-world solutions.

By the end of this training, participants will be able to:

  • Understand the underlying principles of generative AI and large language models.
  • Implement and fine-tune LLMs for specific business applications.
  • Apply prompt engineering techniques for optimal model outputs.
  • Recognize ethical considerations and manage risks in LLM deployment.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction to Generative AI and Large Language Models

  • Overview of generative AI and its evolution
  • Introduction to LLMs: GPT, BERT, and their capabilities
  • Comparing generative models with traditional NLP approaches

Transformer Architecture and Model Training

  • Understanding the transformer architecture in LLMs
  • Self-attention mechanism and language modeling
  • Training large language models and fine-tuning processes

Prompt Engineering for Effective Interaction

  • Crafting prompts for accurate and useful outputs
  • Fine-tuning prompt strategies for varied applications
  • Experimenting with prompt variations to optimize responses

Applications of LLMs in Business

  • Automating customer service with conversational AI
  • Content generation for marketing and media
  • LLMs in data analysis and report generation

Ethical Considerations and Bias Management

  • Identifying potential biases in LLM-generated content
  • Addressing ethical concerns in generative AI applications
  • Strategies for responsible deployment of LLMs

Advanced Techniques in LLMs

  • Fine-tuning LLMs for domain-specific applications
  • Integrating LLMs with other AI systems for enhanced functionality
  • Exploring multilingual and cross-lingual capabilities

The Future of Generative AI in Business

  • Emerging trends in generative AI and LLM research
  • Opportunities and challenges in scaling LLM solutions
  • Preparing for AI-driven transformation in business

Summary and Next Steps

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