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
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Production AI Model Development and Ethics
This course is part of LLM Engineering That Works: Prompting, Tuning, and Retrieval Specialization
Instructor: Professionals from the Industry
Included with
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Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
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.
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Recently updated!
March 2026
Taught in English
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This course is part of the LLM Engineering That Works: Prompting, Tuning, and Retrieval Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
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.
What's included
2 videos2 readings2 assignments2 ungraded labs
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.
What's included
4 videos3 readings5 assignments2 ungraded labs
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.
What's included
4 videos4 readings3 assignments2 ungraded labs
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Frequently asked questions
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.
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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.