Developing and Integrating AI Systems

Description

• Course ID:
  • IC-805-E-04
• Content: This course provides a structured understanding of modern AI systems in an enterprise environment—from the underlying data foundation to productive integration into existing IT landscapes. The focus is on the complete machine learning and AI development lifecycle.
  • Key Topics:
    • End-to-end machine learning workflow (from problem definition to deployment)
    • Data preparation, data quality, and feature engineering
    • Selection of appropriate model types (overview of classical machine learning and deep learning)
    • Model training, validation, and evaluation
    • Fundamentals of training, testing, and validation strategies
    • Introduction to AI system architectures
    • Interfaces between AI models and enterprise systems
    • API-based integration of AI components
    • Introduction to deployment strategies (cloud, on-premises, edge – conceptual overview)
    • Model versioning and reproducibility
    • Fundamentals of MLOps as an operational model for AI systems
    • Quality assurance and risk management in AI engineering
  • Practical Components:
    • Development of a simple machine learning model (conceptual or tool-based)
    • Building an example AI workflow from data ingestion to output generation
    • Integration of a model into a simple application (API demo or low-code approach)
• Prerequisites:
  • Recommended (at least one of the following):
    • IC-805-G-03 – LLM Workshop
    • Basic programming knowledge (e.g., Python or a comparable language)
    • Understanding of data structures and IT systems
    • Not recommended for non-technical business users without a technical background.
• Certification Requirement:
  • Without certification option
• 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:
  • Software developers interested in AI
  • Data scientists and data analysts
  • IT architects and solution architects
  • Technical project managers
  • AI engineers and digital transformation professionals
  • Participants of IC-805-G-03 seeking deeper technical expertise
• Learning Objectives:
  • Participants will be able to:
    • Describe and apply the complete AI/ML development lifecycle
    • Prepare and evaluate data systematically for AI projects
    • Select, train, and evaluate fundamental AI/ML models
    • Integrate AI systems into existing IT architectures
    • Connect simple AI applications through APIs or services
    • Understand requirements related to scalability, maintainability, and quality
    • Explain the role of MLOps throughout the AI lifecycle
• Note:
  • Displayed price excl. VAT