MLOps and AI Operations

Description

• Course ID:
  • IC-805-E-06
• Content: This course introduces the principles, methods, and tools required for the reliable operation of AI and machine learning systems in production environments. The focus is on automation, traceability, quality assurance, and the continuous operation of AI models.
  • Key Topics:
    • Introduction to MLOps as an operational model for AI systems
    • Differences between traditional software development and ML systems
    • CI/CD for AI systems (continuous integration, delivery, and deployment)
    • Model versioning and experiment tracking
    • Reproducibility of AI experiments
    • Model registry and lifecycle management
    • Monitoring AI systems in production environments
    • Drift detection (data and model drift)
    • Quality metrics for AI models in production
    • Automated retraining and update processes
    • Governance in AI operations (traceability and auditability)
    • Introduction to common MLOps toolchains
  • Practical Components:
    • Building a simple MLOps pipeline (conceptual or tool-supported)
    • Monitoring a sample model with drift simulation
    • Versioning and comparing model variants
    • Simulating a CI/CD process for an ML model
• Prerequisites:
  • Required:
    • IC-805-G-04 – Developing and Integrating AI Systems, or
    • Equivalent experience in software development or data science
  • Recommended:
    • IC-805-G-05 – RAG & Enterprise AI Systems (for contextual understanding of GenAI systems)
    • Not suitable for beginners without technical or data-driven experience.
• 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:
  • Machine learning engineers
  • Data scientists with production responsibilities
  • DevOps engineers
  • Software developers working in AI environments
  • AI architects and platform teams
  • Participants of IC-805-G-04 and IC-805-G-05
• Learning Objectives:
  • Participants will be able to:
    • Explain and apply the principles of MLOps
    • Develop reproducible and version-controlled AI models
    • Design CI/CD pipelines for AI systems
    • Implement monitoring strategies for production AI systems
    • Detect and interpret model and data drift
    • Apply fundamental governance requirements in AI operations
    • Evaluate the key components of an MLOps architecture
• Note:
  • Displayed price excl. VAT