AI Engineer (Capstone & Integration Course)

Description

• Course ID:
  • IC-805-E-24
• Content: The AI Systems Architect is responsible for designing, building, and evaluating end-to-end AI systems, from initial concept through to production deployment. The focus is on integrating machine learning models, large language models, and retrieval-augmented generation systems into scalable, reliable, and production-ready architectures.
  • Key focus areas:
    • End-to-end design of AI systems (architecture and implementation) Integration of:
      • Machine Learning (ML) models
      • Large Language Models (LLMs)
      • Retrieval-Augmented Generation (RAG) systems
    • Building production-ready AI systems
    • System design under real-world constraints:
      • Scalability
      • Latency
      • Cost
      • Security
    • Selecting appropriate AI architectures for different use cases
    • MLOps integration in real deployment scenarios
    • Data, model, and service architectures
    • Evaluation of existing AI systems (technical review)
    • Error analysis and optimization strategies
    • Preparation for certification case study
  • Practical exercises include:
    • Designing a complete AI system based on a real-world use case
    • Conducting an architectural review of an existing system
    • Building a reference RAG or ML system
    • Evaluating trade-offs (accuracy vs. cost vs. latency)
• Prerequisites:
  • Required:
    • IC-805-G-01 – Understanding and Safely Applying AI
    • IC-805-G-03 – LLM Workshop
    • IC-805-G-04 – Developing and Integrating AI Systems
    • IC-805-G-05 – RAG & Enterprise AI Systems
    • IC-805-G-06 – MLOps & AI Operations
• Certification Requirement:
  • Proof of attendance of the course and/or prerequisite course(s), or evidence of otherwise acquired equivalent knowledge + Successful completion of the examination + At least 2 years of professional experience in the relevant field
• Conduction Method:
  • Online (For on-site or in-house training, please send us a request via our contact form (Contact - intellcert). We will get in touch with you shortly thereafter.)
• Language:
  • English
• Target  Audience:
  • AI Engineers
  • Data Scientists
  • Software Engineers with an AI focus
  • Solution Architects
  • MLOps Engineers
• Learning Objectives:
  • Participants will be able to:
    • Design and evaluate complete AI systems
    • Integrate ML, LLM, and RAG systems
    • Justify architectural decisions
    • Plan production-ready AI systems
    • Identify and mitigate technical risks
• Note:
  • Displayed price excl. VAT