Google Generative AI in Production
- Código del Curso GO8338
- Duración 1 Día
Otros Métodos de Impartición
Salta a:
Método de Impartición
Este curso está disponible en los siguientes formatos:
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Cerrado
Cerrado
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Clase de calendario
Aprendizaje tradicional en el aula
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Aprendizaje Virtual
Aprendizaje virtual
Solicitar este curso en un formato de entrega diferente.
Temario
Parte superiorTraditional 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.
Products:
- Vertex AI
Updated August 2026
Virtual Learning
This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.
Calendario
Parte superior-
- Método de Impartición: Aprendizaje Virtual
- Fecha: 31 mayo, 2027 | 9:00 AM to 5:00 PM
- Sede: Aula Virtual (W. Europe )
- Idioma: Español
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- Método de Impartición: Aprendizaje Virtual
- Fecha: 06 septiembre, 2027 | 9:00 AM to 5:00 PM
- Sede: Aula Virtual (W. Europe )
- Idioma: Español
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- Método de Impartición: Aprendizaje Virtual
- Fecha: 07 diciembre, 2027 | 9:00 AM to 5:00 PM
- Sede: Aula Virtual (W. Europe )
- Idioma: Español
Dirigido a
Parte superiorDevelopers, DevOps engineers and machine learning engineers who wish to operationalize GenAI-based applications
Objetivos del Curso
Parte superiorAfter this course participants should be able to:
- Understand generative AI operations
- Compare traditional MLOps and GenAIOps
- Analyze the components of an LLM system
- Define and compare RAG and ReAct
- Evaluate application deployment options
- Deploy, package, and version apps
- Maintain and update LLM models
- Test and evaluate gen AI-powered apps
- Deploy CI/CD pipelines for gen AI-powered apps
- Identify security challenges for gen AI applications
- Understand prompt security issues
- Apply sensitive data protection and DLP API
- Implement Model Armor
Contenido
Parte superiorModule 01: Introduction to Generative AI in Production
- Generative AI operations
- Traditional MLOps vs. GenAIOps
- Components of an LLM system
- RAG/ReAct architecture
Module 02: Generative AI Application Deployment
- Application deployment options
- Deployment, packaging, and versioning
- Lab: Deploying an Agentic Application on Cloud Run
Module 03: Productionizing Generative AI
- Maintenance and updates
- Testing and evaluation
- CI/CD pipelines for gen AI-powered apps
- Lab: Tracking Versions of Generative AI Applications
Module 04: Securing Generative AI Applications
- Security challenges
- Prompt security
- Sensitive Data Protection and DLP API
- Model Armor
- Lab: Securing Generative AI-Powered Applications
Pre-requisitos
Parte superiorCompletion of the "Application Development with LLMs on Google Cloud" or equivalent knowledge.
Certificación de Prueba
Parte superiorNone
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