AI Agents in Production: From Prototype to Reality - Part 10
This blog post, the tenth and final installment in a series on AI agents, focuses on deploying AI agents to production. It covers evaluating agent performance, addressing common issues, and managing costs. The post emphasizes the importance of a robust evaluation system, providing potential solutions for performance issues, and outlining cost management strategies such as response caching, using smaller models, and implementing router models.Embracing Responsible AI: Measure and Mitigate Risks for a Generative AI App in Azure AI Studio
Artificial intelligence has taken the world by storm, redefining the way businesses operate and innovate. Whether you're an experienced developer or a beginner looking to break into the world of AI, Azure AI Studio offers a robust platform for creating cutting-edge AI applications responsibly and securely. I recently had the opportunity to dive into the Microsoft Learn module: Measure and Mitigate Risks for a Generative AI App in Azure AI Studio. It’s an incredible resource that walks you through every step of building and refining a responsible AI application. Today, I’d like to share my experience and encourage you to embark on this journey too, gaining essential skills in the process.Evaluating Language Models with Azure AI Studio: A Step-by-Step Guide
Evaluating language models is a crucial step in achieving this goal. By assessing the performance of language models, we can identify areas of improvement, optimize their performance, and ensure that they are reliable and accurate. However, evaluating language models can be a challenging task, requiring significant expertise and resources.Evaluating Generative AI Models with Azure Machine Learning
LLM evaluation assesses the performance of a large language model on a set of tasks, such as text classification, sentiment analysis, question answering, and text generation. The goal is to measure the model's ability to understand and generate human-like language.
