This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production.
Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Vertex AI platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.
Overview
Syllabus
- Welcome to the Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
- This module covers the course objectives and provides an overview of the course structure.
- Introduction to Model Evaluation
- This module introduces model evaluation challenges and solution offerings by Vertex AI.
- Model Evaluation for Generative AI
- This module describes challenges of evaluating the Generative AI tasks and best practices to overcome these challenges. The module also covers the different types of model evaluation services available in Vertex AI and then introduces Vertex AI Automatic Metrics, Automatic Side by Side and Safety Bias evaluation services.
- Course Summary
- This module provides a summary of the entire course by covering the most important concepts, tools, technologies, and products.
Taught by
Google Cloud Training
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Machine Learning Operations (MLOps): Getting Started
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Introduction to Vertex AI Studio
