Course Overview of Generative AI in Production

This instructor-led course focuses on the operationalization and deployment of Generative AI-powered applications on Google Cloud. Participants will explore the differences between traditional MLOps and emerging GenAIOps practices, deployment strategies for LLM applications, production management, observability, and security considerations for enterprise-scale Generative AI systems. 

Through hands-on labs and real-world scenarios, learners will gain practical experience in deploying, versioning, securing, monitoring, and maintaining Generative AI applications using Google Cloud services such as Vertex AI, Cloud Run, Cloud Logging, Cloud Monitoring, and Cloud Trace. The course also covers best practices for production-grade LLM systems, CI/CD automation, and AgentOps implementation. 

After completing Generative AI in Production, students will be able to:

  • Understand the challenges in productionizing Generative AI applications.
  • Differentiate Traditional MLOps and GenAIOps practices
  • Analyze the components of enterprise LLM systems
  • Understand RAG and ReAct architectures
  • Deploy and version LLM-powered applications
  • Implement CI/CD pipelines for Generative AI applications
  • Secure GenAI applications using DLP API and Model Armor
  • Implement logging, monitoring, and observability for production system
  • Utilize AgentOps and Agent Analytics concepts
  • Apply production best practices for scalable LLM systems

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Key Features of Generative AI in Production

  • Practical, Hands-On Learning 

     

  • Comprehensive GenAIOps Coverage 

     

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  • Production Deployment Strategies 

     

  •  Security and Governance 

     

  • Observability and Monitoring 

     

  •  CI/CD and Automation 

     

  •  Enterprise Production Best Practices 

     

Who should Attend Generative AI in Production

  • Developers
  • DevOps Engineers
  • Machine Learning Engineers
  • Platform Engineers
  • GenAI Engineers
  • AI Application Developers
  • Cloud Architects working with Generative AI systems

Prerequisites of Generative AI in Production

  • Completion of “Application Development with LLMs on Google Cloud” or equivalent knowledge
  • Basic understanding of Generative AI concepts
  • Familiarity with Google Cloud fundamentals
  • Basic knowledge of APIs and application deployment
  • Recommended understanding of Python and REST APIs

    • Learning Objective of Generative AI in Production

    • Understand the concepts and challenges of Generative AI Operations (GenAIOps)
    • Differentiate Traditional MLOps and production requirements for LLM systems
    • Understand deployment strategies for Generative AI applications
    • Learn packaging, versioning, and CI/CD for GenAI applications
    • Implement security controls such as DLP API and Model Armor
    • Understand logging, monitoring, and observability for production AI systems
    • Utilize Cloud Operations tools for monitoring and troubleshooting
    • Implement AgentOps and Agent Analytics for intelligent systems
    • Apply enterprise best practices for operationalizing LLM-powered applications

    Why choose CloudThat as your training partner for Generative AI in Production

    •  Specialized GCP Focus  CloudThat specializes in cloud technologies and offers focused, industry-aligned Google Cloud training programs. As Authorized Google Cloud Trainers, we provide in-depth coverage of Google Cloud services, best practices, real-world use cases, and hands-on experience. 
    •  Industry-Recognized Trainers  Our trainers are certified Google Cloud professionals with extensive real-world experience in cloud architecture, AI/ML, DevOps, and Generative AI implementations. 
    • Hands-On Learning Approach  CloudThat emphasizes practical learning through hands-on labs, real-world scenarios, demos, and implementation-focused exercises to help learners gain production-ready skills
    •  Customized Learning Paths  We offer training paths tailored for beginners, intermediate learners, and advanced professionals to help participants achieve their learning goals effectively. 
    • Interactive Learning Experience  Training sessions include live demonstrations, discussions, assessments, and interactive exercises to maximize engagement and retention. 
    • Career and Certification Support  CloudThat provides guidance for certification preparation, interview readiness, and practical project experience to support learners in their professional growth. 
    • Continuous Updates and Industry Alignment  Our course content is continuously updated to align with the latest Google Cloud technologies, Generative AI trends, and enterprise best practices. 
    • Positive Learner Feedback  Thousands of professionals globally trust CloudThat for quality cloud and AI training programs backed by strong learner reviews and success stories. 

    Course Outline for Generative AI in Production Download Course Outline

    Lecture Content

    • Introduction to Generative AI Operations
    • Traditional MLOps vs GenAIOps
    • Components of an LLM System
    • RAG Architecture
    • ReAct Architecture
    • Enterprise Production Challenges

    Lab Content

    • NA

    Lecture Content

    • Application Deployment Options
    • Packaging and Versioning Strategies
    • Deployment Architectures for LLM Apps
    • Cloud Run Deployment Concepts
    • Production Deployment Best Practices

    Lab Content

    • Lab: Deploying an Agentic Application on Cloud Run

    Lecture Content

    • Maintenance and Model Updates
    • Testing and Evaluation Techniques
    • CI/CD Pipelines for GenAI Applications
    • Version Tracking and Rollbacks
    • Production Best Practices

    Lab Content

    • Lab: Tracking Versions of Generative AI Applications

    Lecture Content

    • Security Challenges in GenAI Applications
    • Prompt Injection and Prompt Security
    • Sensitive Data Protection Concepts
    • DLP API Integration
    • Implementing Model Armor
    • Security Best Practices for LLM Systems

    Lab Content

    • Lab: Securing Generative AI-Powered Applications

    Lecture Content

    • Google Cloud Observability Overview
    • Cloud Operations Concepts
    • Cloud Logging
    • Cloud Monitoring
    • Cloud Trace
    • Agent Analytics and AgentOps
    • Monitoring and Troubleshooting Production Systems

    Lab Content

    • Lab: Logging, Monitoring, and Agent Analytics

    Certification Details of Generative AI in Production

      CloudThat Course Completion Certificate

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    Course ID: 28194

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    FAQs for Generative AI in Production

    GenAIOps refers to the practices and processes used to deploy, manage, monitor, secure, and operationalize Generative AI-powered applications in production environments.

    The course covers GenAIOps, deployment strategies, CI/CD, observability, monitoring, security, prompt protection, DLP API, Model Armor, and production management of LLM systems.

    The course uses Vertex AI, Cloud Run, Cloud Logging, Cloud Monitoring, Cloud Trace, DLP API, and Cloud Operations Suite.

    Yes, the course includes multiple hands-on labs focused on deployment, versioning, security, logging, and monitoring.

    Basic understanding of Python, APIs, and Google Cloud fundamentals is recommended.

    Yes, the course includes prompt security, sensitive data protection, DLP API usage, and Model Armor implementation.

    Yes, the course covers Cloud Logging, Cloud Monitoring, Cloud Trace, Agent Analytics, and observability best practices.

    The course duration is 1 day (approximately 480 minutes).

    Developers, DevOps engineers, ML engineers, GenAI engineers, and cloud professionals interested in operationalizing Generative AI applications.

    Yes, the course includes CI/CD pipelines, version tracking, testing, and deployment automation for production-grade Generative AI systems.

    Enquire Now