Azure AI & ML: Optimize Language Models for AI Applications

本课程是 Exam Prep DP-100: Microsoft Azure Data Scientist Associate 专项课程的一部分

位教师：Whizlabs Instructor

包含在中

了解更多

2个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

5 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Exam Prep DP-100: Microsoft Azure Data Scientist Associate 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有2个模块

This course is designed to provide a comprehensive foundation in Azure Machine Learning, equipping learners with the skills to deploy, manage, and optimize ML models efficiently. Participants will begin by exploring model deployment and consumption in Azure ML, understanding how to operationalize machine learning solutions in production environments.

The course progresses to managing and evaluating models, covering key concepts such as performance monitoring, retraining strategies, and best practices for ensuring model accuracy. Learners will gain expertise in Azure AutoML workflows, from data preparation to model selection and evaluation, ensuring automated yet effective ML development. Additionally, the course covers key aspects of MLOps, enabling seamless integration with Azure services for scalable and secure machine learning operations. This course is structured into multiple modules, each featuring lessons and video lectures that provide theoretical insights and hands-on practice. Participants will engage with approximately 3:00–4:00 hours of instructional content, ensuring both conceptual understanding and practical application. To reinforce learning, graded and ungraded assignments are included within each module to test the ability of learners in real-world scenarios. Module 1: Azure AI Foundry: End-to-End Model Development & Optimization Module 2: Optimize model training with Azure Machine Learning By end of this course, you will be able to learn Understand the concepts of Azure AI Foundry, including its role in model optimization, fine-tuning, and retrieval-augmented generation (RAG) strategies. Learn how to explore and manage the Model Catalog and Collections within Azure AI Foundry and ML, and use compute resources effectively. Gain practical experience testing and manually evaluating prompts in the Azure AI Foundry portal playground, including tracking prompt variants. Discover how to create and configure search indexes in the Azure portal, using Azure AI Search for enhanced data retrieval and model deployment.

This module provides a comprehensive understanding of Azure AI Foundry and its capabilities, equipping learners with the skills to leverage AI models for advanced applications. Participants will explore key concepts such as Retrieval Augmented Generation (RAG) for enhancing AI-driven responses, fine-tuning strategies for optimizing model performance, and best practices for deploying AI models in production environments. The module covers the Azure AI Foundry model catalog, compute considerations, and how to test and refine language models using the interactive playground. Learners will gain expertise in manually evaluating prompts, defining and tracking prompt variants, and utilizing Azure AI Search to create efficient search indexes. By the end of this module, participants will be prepared to work with Azure AI Foundry and ML tools, ensuring scalable and high-performing AI solutions for various enterprise applications.

涵盖的内容

9个视频2篇阅读材料2个作业1个讨论话题

9个视频总计50分钟

Azure AI Foundry: Overview and Demo5分钟
Retrieval Augmented Generation (RAG) in Azure AI and ML: Overview4分钟
Optimizing Models: Fine-Tuning, RAG and Application Strategies6分钟
Model Catalog and Collections [Azure AI Foundry and ML]-Overview4分钟
Model Catalog and Collections [Azure AI Foundry and ML]-Compute3分钟
Test a deployed language model in the playground6分钟
How to manually evaluate prompts in Azure AI Foundry portal playground5分钟
Define and track prompt variants4分钟
Quickstart: Create a search index in the Azure portal - Azure AI Search8分钟

2篇阅读材料总计60分钟

Welcome to the Course30分钟
Azure AI Foundry: End-to-End Model Development & Optimization - Overview30分钟

2个作业总计70分钟

Azure AI Foundry: End-to-End Model Development & Optimization - Graded Assignment40分钟
Optimize language models for AI applications - Practice Assignment30分钟

1个讨论话题总计20分钟

Meet & Greet20分钟

This module provides a comprehensive understanding of preparing machine learning workflows for production using Azure Machine Learning, equipping learners with the skills needed for scalable and efficient deployment. Participants will explore best practices for transitioning from notebooks to scripts, executing command jobs with parameters, and integrating MLflow for model tracking and evaluation. The module covers pipeline creation, custom components, and prebuilt workflows—including an Automobile Price Prediction pipeline—to automate and optimize ML processes. Learners will gain expertise in working with metrics, hyperparameters, and data transformation techniques, ensuring model performance and reliability. Additionally, the module emphasizes key aspects of production readiness, such as managing resources, tracking ML models, and refining training workflows for real-world applications. By the end of this module, participants will be equipped with practical knowledge to implement and manage robust ML pipelines within Azure Machine Learning effectively

涵盖的内容

19个视频2篇阅读材料3个作业

19个视频总计118分钟

Preparing code for production scenarios8分钟
Convert a notebook to a script7分钟
Run a script as a command job7分钟
Use parameters in a command job6分钟
Exploring The use of MLflow For Tracking Models6分钟
Track Metrics with Machine Learning Flow6分钟
Integrating ML Flow in Model Training Flow6分钟
Viewing Metrics and Evaluating Models8分钟
Creating and Using components in Azure Machine Learning5分钟
Creating Pipelines in Azure Machine Learning5分钟
Creating a Custom Pipeline6分钟
Components in Azure Machine Learning6分钟
Prebuilt Pipelines (Automobile Price Prediction)9分钟
Understanding Metrics4分钟
Load and Transform Data in Azure Machine Learning using notebooks4分钟
Parameters and Hyperparameters4分钟
Key Hyperparameters in Machine Learning5分钟
Exam Tips4分钟
Conclusion, What's Next, Job Roles, and Best Practices3分钟

2篇阅读材料总计60分钟

Preparing Code, Integrating ML Flow, and Tracking Models - Overview30分钟
Key Takeaways30分钟

3个作业总计100分钟

Optimize model training with Azure Machine Learning - Graded Assignment40分钟
Preparing Code, Integrating MLflow, and Tracking Models - Practice Assignment30分钟
Building Pipelines, Components, and Optimizing Models - Practice Assignment30分钟

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Whizlabs Instructor

Whizlabs

138 门课程95,945 名学生

提供方

Whizlabs

从 Machine Learning 浏览更多内容

Whizlabs
Azure ML: Designing and Preparing Machine Learning Solutions
课程
Whizlabs
Azure ML: Deploying, Managing, and Experimenting with Models
课程
Whizlabs
Azure ML: Explore & Configure the Machine Learning Workspace
课程

人们为什么选择 Coursera 来帮助自己实现职业发展

Felipe M.

自 2018开始学习的学生

''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'

Jennifer J.

自 2020开始学习的学生

''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'

Larry W.

自 2021开始学习的学生

''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'

Chaitanya A.

''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'

通过 Coursera Plus 开启新生涯

无限制访问 10,000+ 世界一流的课程、实践项目和就业就绪证书课程 - 所有这些都包含在您的订阅中

了解更多

通过在线学位推动您的职业生涯

获取世界一流大学的学位 - 100% 在线

探索学位

加入超过 3400 家选择 Coursera for Business 的全球公司

提升员工的技能，使其在数字经济中脱颖而出

了解更多

常见问题

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.