Applying and implementing LLM

At the end of the course, you will earn a Certificate of Completion from Spirit in Projects.

Are you eager to unlock the full potential of Large Language Models (LLMs) and do you want to seamlessly integrate them into your existing systems? If you are keen to explore diverse application scenarios and collaborate on crafting practical solutions, this training is for you. From text classification to system integration, you will learn how to effectively embed LLMs into your infrastructure, leverage them for various tasks and develop custom solutions tailored to your real-world challenges.

Dates and times AI Expert Back to Professionals

Advising

Objectives

  • Experiencing various scenarios applying LLM
  • Implementing cutting-edge NLP solutions
  • Integrating LLMs seamlessly into existing system architectures
  • Develop a deep understanding of the strengths and limitations of different model types
  • Design and implement custom LLM-based solutions tailored to your needs

Target groups:

AI Expert, Software Developer, System Architect, Software Architect, Data Scientist, Machine Learning Engineer, NLP Specialist, AI Engineer and anyone eager to apply and integrate LLMs in their business

Dates and times

Syllabus

1. An introduction to the integration of LLMs

  • Architectural patterns for LLM-based systems
  • REST APIs and micro services with LLMs
  • Scalable infrastructures for LLM-based applications
  • Various deployment options (cloud, on-premise, hybrid)
  • Governance and security
  • In groups: Build a scalable LLM service architecture

2. Text classification and sentiment analysis

  • Building classification pipelines
  • Feature extraction with transformer models
  • Integration into content management systems
  • Batch and real-time processing
  • In groups: Integrate a sentiment analyzer into an existing web application

3. Extracting information and process documents

  • Multilingual NER systems
  • Applying document processing pipelines
  • Link document management systems
  • Workflow integration
  • In groups: Developing a document processing pipeline with LLM integration

4. Question & Answering and retrieval systems

  • Architecture of QA systems
  • Leverage vector databases
  • Link QA systems to existing knowledge repositories
  • Caching strategies and performance optimization
  • In groups: Developing an integrated enterprise search system supported by LLMs

5. Chatbots and dialogue systems

  • Messaging platforms
  • CRM systems
  • Context management and session handling
  • Monitoring and logging
  • In groups: Integrating an LLM-powered chatbot into a corporate platform

6. System integration and deployment

  • API design and API management
  • Monitoring
  • Cost control
  • A/B testing and gradual rollout
  • Error handling and fallback strategies
  • In groups: Implementing a complete LLM-based service with built-in monitoring and failover mechanisms

Advising after course completion

Spirit in Projects