Training Google Cloud

Training goals

code: G-GAIP | version: 1.0

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.

 

What you'll 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.

 

Audience

This course is intended for: developers and machine learning engineers who wish to operationalize Gen AI-based applications.

 

Products

  • Vertex AI
  • Vertex AI Pipelines
  • Vertex AI Evaluation
  • Vertex AI Studio
  • Vertex AI Gemini API
  • Gemini

Conspect Show list

  • Introduction to Generative AI in Production
    • Topics
      • AI System Demo: Coffee on Wheels
      • Traditional MLOps vs. GenAIOps
      • Generative AI Operations
      • Components of an LLM System
    • Objectives
      • Understand generative AI operations
      • Compare traditional MLOps and GenAIOps
      • Analyze the components of an LLM system
  • Managing Experimentation
    • Topics
      • Datasets and Prompt Engineering
      • RAG and ReACT Architecture
      • LLM Model Evaluation (metrics and framework)
      • Tracking Experiments
    • Objectives
      • Experiment with datasets and prompt engineering.
      • Utilize RAG and ReACT architecture.
      • Evaluate LLM models.
      • Track experiments.
    • Activities
      • Lab: Unit Testing Generative AI Applications
      • Optional Lab: Generative AI with Vertex AI: Prompt Design
  • Productionizing Generative AI
    • Topics
      • Deployment, packaging, and versioning (GenAIOps)
      • Testing LLM systems (unit and integration)
      • Maintenance and updates (operations)
      • Prompt security and migration
    • Objectives
      • Deploy, package, and version models
      • Test LLM systems
      • Maintain and update LLM models
      • Manage prompt security and migration
    • Activities
      • Lab: Vertex AI Pipelines: Qwik Start
      • Lab: Safeguarding with Vertex AI Gemini API
  • Logging and Monitoring for Production LLM Systems
    • Topics
      • Cloud Logging
      • Prompt versioning, evaluation, and generalization
      • Monitoring for evaluation-serving skew
      • Continuous validation
    • Objectives
      • Utilize Cloud Logging
      • Version, evaluate, and generalize prompts
      • Monitor for evaluation-serving skew
      • Utilize continuous validation
    • Activities
      • Lab: Vertex AI: Gemini Evaluations Playbook
      • Optional Lab: Supervised Fine Tuning with Gemini for Question and Answering
Download conspect training as PDF

Additional information

Prerequisites

Completion of "Introduction to Developer Efficiency on Google Cloud" or equivalent knowledge.
Difficulty level
Duration 1 day
Certificate

The participants will obtain certificates signed by Google Cloud (course completion).
Trainer

Authorized Google Cloud Trainer

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