🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
GCP300GENAIPROD

Generative AI in Production

Traditional MLOps is a set of practices to productionize traditional ML systems for enterprise applications. Generative AI raises new challenges in managing and productionizing applications at scale. The field of generative AI operations seeks to address these new challenges. In this course, you learn about the challenges that arise when deploying and productionizing generative AI-powered applications. You learn how to secure your generative AI-powered applications. Finally, you will discuss best practices for logging and monitoring your generative AI-powered applications in production.

Google Cloud
✓ Official training Google CloudLevel Advanced⏱️ 1 day (7h)

What you will learn

  • Productionize LLM-powered applications
  • Secure generative AI applications
  • Implement logging and monitoring for LLM-powered applications

Prerequisites

  • Completion of the 'Application Development with LLMs on Google Cloud' or equivalent knowledge.

Target audience

  • Developers, DevOps engineers and machine learning engineers who wish to operationalize GenAI-based applications

Training Program

5 modules to master the fundamentals

Objectives
  • Understand generative AI operations
  • Compare traditional MLOps and GenAIOps
  • Analyze the components of an LLM system
  • Define and compare RAG and ReAct
Topics covered
  • →Generative AI operations
  • →Traditional MLOps vs. GenAIOps
  • →Components of an LLM system
  • →RAG/ReAct architecture
Objectives
  • Evaluate application deployment options
  • Deploy, package, and version apps
Topics covered
  • →Application deployment options
  • →Deployment, packaging, and versioning
Objectives
  • Maintain and update LLM models
  • Test and evaluate gen AI-powered apps
  • Deploy CI/CD pipelines for gen AI-powered apps
Topics covered
  • →Maintenance and updates
  • →Testing and evaluation
  • →CI/CD pipelines for gen AI-powered apps
Activities

Lab: Tracking Versions of Generative AI Applications

Objectives
  • Identify security challenges for gen AI applications
  • Understand prompt security issues
  • Apply sensitive data protection and DLP API
  • Implement Model Armor
Topics covered
  • →Security challenges
  • →Prompt security
  • →Sensitive Data Protection and DLP API
  • →Model Armor
Objectives
  • Describe the purpose and capabilities of Google Cloud Observability
  • Explain the purpose of Cloud Monitoring
  • Explain the purpose of Cloud Logging
  • Explain the purpose of Cloud Trace
Topics covered
  • →Cloud Operations
  • →Cloud Logging
  • →Monitoring
  • →Cloud Trace
  • →Agent Analytics and AgentOps
  • →Putting it all together

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Upcoming sessions

December 11, 2026
Distanciel • Français
Register

Quality Process

SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach

Teaching Methods Used
  • Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
  • Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
  • Guided Labs — Guided practical exercises on software, hardware, or technical environments.
  • Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System

The achievement of training objectives is evaluated at multiple levels to ensure quality:

  • Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
  • Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
  • Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.

790€ excl. VAT

per learner

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