This comprehensive program provides end-to-end training on the production machine learning lifecycle, designed to take your models from experiment to deployment. You’ll progress from applying feature engineering pipelines with scikit-learn and selecting models through rigorous evaluation, to optimizing PyTorch models with custom training loops and advanced diagnostics. Finally, you will master the principles of responsible AI by creating model cards and auditing systems for ethical compliance. By the end of this course, you will be able to build, tune, and deploy efficient, reliable, and ethical AI solutions. These skills are essential for ML engineers who develop and maintain robust, production-grade machine learning systems.
Production AI Model Development and Ethics
Production AI Model Development and Ethics
包含在 中
您将学到什么
Apply custom training loops with callbacks (early-stopping, checkpointing) and diagnose gradient issues using norm and activation analysis.
Implement feature engineering pipelines for structured and text data, then evaluate ML experiments to select production-ready models.
Create comprehensive model cards for LLM features that detail intended use, technical limitations, and specific fairness metrics.
Evaluate AI systems against established ethical guidelines to identify biases and propose actionable mitigation strategies.
您将获得的技能
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 LLM Engineering That Works: Prompting, Tuning, and Retrieval 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3个模块
This module is for machine learning practitioners and data scientists who are ready to move beyond notebooks and build production-grade ML systems. Getting a model to work once is easy; making it reliable, reproducible, and efficient in production is the real challenge. This module provides the engineering discipline to bridge that gap. By the end, you will not only be building models, but also be capable of engineering reliable, efficient, and production-worthy ML systems.
涵盖的内容
2个视频2篇阅读材料2个作业2个非评分实验室
This module introduces the core concepts of PyTorch Lightning that streamline deep learning development. You will learn why refactoring from raw PyTorch is essential for building scalable, production-ready models. You will get hands-on experience structuring your code into a LightningModule and using the Trainer to handle the engineering boilerplate, allowing you to focus purely on the science.
涵盖的内容
4个视频3篇阅读材料5个作业2个非评分实验室
This module equips engineers, auditors, and AI practitioners with the concrete skills to move from ethical principles to engineering practice. You will learn to create comprehensive model cards that document a system's intended use, dataset origins, performance metrics, and limitations, ensuring every stakeholder understands what the system does and where it might fail.
涵盖的内容
4个视频4篇阅读材料3个作业2个非评分实验室
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
267 门课程 38,605 名学生
提供方
从 Machine Learning 浏览更多内容
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。'
常见问题
This course is aimed at ML engineers and practitioners who already know Python and basic ML concepts. If you are new to machine learning, consider completing an introductory ML course first.
You will work with scikit-learn for feature pipelines and experiment evaluation, and PyTorch for custom training loops and diagnostics. The course also references tools and formats used for model cards and ethical audits.
The course focuses on the practical tasks you encounter when productionizing models: building reproducible data pipelines, selecting models under real-world constraints, optimizing training stability, and documenting ethical considerations. These skills help you deploy reliable, maintainable AI systems.
更多问题
提供助学金，
¹ 本课程的部分作业采用 AI 评分。对于这些作业，将根据 Coursera 隐私声明使用您的数据。