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.



Machine Learning Operations with Vertex AI: Model Evaluation

Instructor: Google Cloud Training
Access provided by Coursera Learning Team
What you'll learn
Understand the nuances of model evaluation in both predictive and generative AI, recognizing its crucial role within the MLOps lifecycle.
Identify and apply appropriate evaluation metrics for different generative AI tasks.
Efficiently evaluate generative AI with Vertex AI's diverse evaluation services, including both computation-based and model-based methods.
Implement best practices for LLM evaluation, to ensure robust and reliable model deployment in production environments.
Details to know

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There are 4 modules in this course
This module covers the course objectives and provides an overview of the course structure.
What's included
1 video
This module introduces model evaluation challenges and solution offerings by Vertex AI.
What's included
3 videos1 reading1 assignment
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.
What's included
7 videos1 reading1 assignment
This module provides a summary of the entire course by covering the most important concepts, tools, technologies, and products.
What's included
1 video
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