Course Overview of Generative AI in Production

This instructor-led course provides a comprehensive understanding of deploying and managing Generative AI applications in production environments. Learners will explore key concepts such as LLM architectures, deployment strategies, security practices, observability, and CI/CD pipelines for Gen AI systems. Through hands-on labs and real-world use cases, participants will gain practical experience in building scalable, secure, and production-ready AI-powered applications. 

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

  • Understand the fundamentals of Generative AI in production.
  • Compare traditional MLOps with GenAIOps practices.
  • Design and deploy LLM-based applications.
  • Implement CI/CD pipelines for AI-powered applications.
  • Apply prompt security and data protection strategies.
  • Secure Generative AI applications using best practices.
  • Monitor and troubleshoot production LLM systems.
  • Implement observability using logging, monitoring, and tracing tools.

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Key Features of Generative AI in Production

  • 7 Learning Modules covering GenAI lifecycle from deployment to observability.

  • Hands-On Labs focused on real-world implementation scenarios. 

  • Deployment Strategies using Cloud Run and modern infrastructure.

  • Security Best Practices including DLP and prompt security. 

  • Observability Techniques using Cloud Logging, Monitoring, and Trace. 

  • Industry-Relevant Use Cases and production-ready architectures. 

Prerequisites of Generative AI in Production

  • Basic understanding of cloud concepts and APIs is recommended.
  • Familiarity with machine learning or AI concepts will be helpful but is not mandatory.

    • Learning Objective of Generative AI in Production

    • To enable learners to design, deploy, secure, and monitor Generative AI applications in production using modern tools, architectures, and best practices. 

    Course Outline for Generative AI in Production Download Course Outline

    Lecture Content

    • Course Overview
    • Learning Objectives
    • Tools and Environment Setup

    Lab Content

    • Orientation / Setup Activity

    Lecture Content

    • Generative AI Operations
    • Traditional MLOps vs GenAIOps
    • Components of an LLM System
    • RAG / ReAct Architecture

    Lab Content

    • NA

    Lecture Content

    • Application Deployment Options
    • Deployment Strategies
    • Packaging and Versioning

    Lab Content

    • Lab: Deploying an Agentic Application on Cloud Run

    Lecture Content

    • Maintenance and Updates
    • Testing and Evaluation
    • CI/CD Pipelines for Gen AI Apps
    • Prompt Security and Migration

    Lab Content

    • Lab: Managing Versions of Generative AI Applications

    Lecture Content

    • Security Challenges in Generative AI
    • Prompt Security Best Practices
    • Sensitive Data Protection using DLP API
    • Model Armor

    Lab Content

    • Lab: Securing Generative AI-Powered Applications

    Lecture Content

    • Cloud Operations Overview
    • Cloud Logging
    • Monitoring
    • Cloud Trace
    • Agent Analytics and AgentOps
    • End-to-End Observability

    Lab Content

    • Lab: Logging, Monitoring, and Agent Analytics

    Lecture Content

    • Key Takeaways
    • Best Practices
    • Real-World Implementation Guidance

    Lab Content

    • Final Discussion / Wrap-up

    Certification Details of Generative AI in Production

      Course Completion Certificate

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    Course ID: 28194

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    FAQs for Generative AI in Production

    Developers, data engineers, AI practitioners, and architects looking to deploy GenAI solutions in production.

    GenAIOps, LLM architectures, deployment, security, observability, and CI/CD pipelines.

    Basic understanding of programming is helpful but not mandatory.

    3 days instructor-led training.

    Yes, multiple labs focused on real-world scenarios.

    Yes, including prompt security and data protection.

    Yes, including Cloud Run-based deployment.

    Enquire Now