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.

✓ Formation officielle Google CloudNiveau Advanced⏱️ 1 jour (7h)

Ce que vous allez apprendre

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.

Prérequis

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

Public cible

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

Programme de la Formation

4 modules pour maîtriser les fondamentaux

Objectifs

Understand generative AI operations
Compare traditional MLOps and GenAIOps
Analyze the components of an LLM system

Sujets abordés

→AI System Demo: Coffee on Wheels
→Traditional MLOps vs. GenAIOps
→Generative AI Operations
→Components of an LLM System

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.