🏆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

In this course, you learn about the different challenges that arise when productionizing generative AI-powered applications versus traditional ML. You will learn how to manage experimentation and tuning of your LLMs, then you will discuss how to deploy, test, and maintain your LLM-powered applications. Finally, you will discuss best practices for logging and monitoring your LLM-powered applications in production.

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

What you will learn

  • Describe the challenges in productionizing applications using generative AI.
  • Manage experimentation and evaluation for LLM-powered applications.
  • Productionize LLM-powered applications.
  • Implement logging and monitoring for LLM-powered applications.

Prerequisites

  • Completion of "Introduction to Developer Efficiency on Google Cloud" or equivalent knowledge.

Target audience

  • Developers and machine learning engineers who wish to operationalize Gen AI-based applications

Training Program

4 modules to master the fundamentals

Objectives
  • Understand generative AI operations
  • Compare traditional MLOps and GenAIOps
  • Analyze the components of an LLM system
Topics covered
  • →AI System Demo: Coffee on Wheels
  • →Traditional MLOps vs. GenAIOps
  • →Generative AI Operations
  • →Components of an LLM System
Objectives
  • Experiment with datasets and prompt engineering.
  • Utilize RAG and ReACT architecture.
  • Evaluate LLM models.
  • Track experiments.
Topics covered
  • →Datasets and Prompt Engineering
  • →RAG and ReACT Architecture
  • →LLM Model Evaluation (metrics and framework)
  • →Tracking Experiments
Activities

Lab: Unit Testing Generative AI Applications

Optional Lab: Generative AI with Vertex AI: Prompt Design

Objectives
  • Deploy, package, and version models
  • Test LLM systems
  • Maintain and update LLM models
  • Manage prompt security and migration
Topics covered
  • →Deployment, packaging, and versioning (GenAIOps)
  • →Testing LLM systems (unit and integration)
  • →Maintenance and updates (operations)
  • →Prompt security and migration
Activities

Lab: Vertex AI Pipelines: Qwik Start

Lab: Safeguarding with Vertex AI Gemini API

Objectives
  • Utilize Cloud Logging
  • Version, evaluate, and generalize prompts
  • Monitor for evaluation-serving skew
  • Utilize continuous validation
Topics covered
  • →Cloud Logging
  • →Prompt versioning, evaluation, and generalization
  • →Monitoring for evaluation-serving skew
  • →Continuous validation
Activities

Lab: Vertex AI: Gemini Evaluations Playbook

Optional Lab: Supervised Fine Tuning with Gemini for Question and Answering

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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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