[100% Off] Evaluating Generative Models: Methods, Metrics &Amp; Tools
Optimize AI applications with advanced LLM evaluation techniques like Automatic Metrics and AutoSxS for better results.
What you’ll learn
- Understand the Fundamentals of LLM Evaluation
- Master Vertex AI Evaluation Tools
- Apply Advanced Evaluation Methods
- Evaluate Non-Text Generative AI Models
Requirements
- Basic Understanding of AI and Machine Learning
- No Programming Skills Required
- Familiarity with Large Language Models
- Familiarity with Cloud Platforms
Description
In this course, you will master advanced evaluation techniques for Large Language Models (LLMs) using tools like Automatic Metrics and AutoSxS. These evaluation methods are critical for optimizing AI models and ensuring their effectiveness in real-world applications. By taking this course, you will receive valuable knowledge and practical skills, including:
-
Hands-on experience with Google Cloud’s Vertex AI to evaluate LLMs using powerful, industry-standard evaluation tools.
-
Learn to use Automatic Metrics to assess model output quality for tasks like text generation, summarization, and question answering.
-
Master AutoSxS to compare multiple models side by side, gaining deeper insights into model performance and selecting the best-suited models for your tasks.
-
Apply evaluation techniques to improve AI applications across various industries, such as healthcare, finance, and customer service.
-
Understand fairness evaluation metrics to ensure that AI models produce equitable and unbiased outcomes, addressing critical challenges in AI decision-making.
-
Prepare for future AI trends by learning about evolving evaluation tools and services in the context of generative AI.
-
Optimize your model selection and deployment strategies, enhancing AI solution performance, efficiency, and fairness.
By the end of this course, you will have the ability to:
-
Evaluate LLMs effectively to optimize their performance.
-
Make data-driven decisions for selecting the best models for your applications.
-
Ensure fairness in AI systems, mitigating biases and improving outcomes.
-
Stay ahead of AI evaluation trends to future-proof your skills in a rapidly evolving field.
