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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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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

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.

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

2 videos Total 11 minutes

How to Build a ColumnTransformer: Step-by-Step 7 minutes
Why a High Accuracy Score Can Be a Lie 4 minutes

2 readings Total 14 minutes

The What and How of Scikit-learn Pipelines 7 minutes
From Evaluation to Recommendation 7 minutes

2 assignments Total 60 minutes

AI Graded Open-Ended Questions 30 minutes
Submit Your Feature Engineering and Evaluation Report 30 minutes

2 ungraded labs Total 82 minutes

Build a Pipeline for Churn Prediction 22 minutes
How to Diagnose Overfitting with TensorBoard 60 minutes

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

4 videos Total 26 minutes

Building Your First LightningModule 6 minutes
Implementing Callbacks in the Trainer 7 minutes
When Training Goes Wrong: The Exploding Gradient 7 minutes
Monitoring Gradients with a Custom Callback 6 minutes

3 readings Total 15 minutes

The Core Components: LightningModule and Trainer 5 minutes
What are Callbacks? EarlyStopping and ModelCheckpointing 5 minutes
What to Look For: Diagnosing Instability with Gradients 5 minutes

5 assignments Total 60 minutes

Hands-On Learning: Refactoring Steps for a BERT LightningModule 15 minutes
Knowledge Check: Lightning Components 5 minutes
Knowledge Check: Callback Configuration 5 minutes
Knowledge Check: Diagnostic Scenarios 5 minutes
Final Project: Fine-Tune, Diagnose, and Deploy 30 minutes

2 ungraded labs Total 120 minutes

Hands-On: Implement Early Stopping and Cloud Checkpointing 60 minutes
Hands-On: Build and Use a Gradient Monitoring Callback 60 minutes

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

4 videos Total 28 minutes

Anatomy of a Model Card 7 minutes
From Data to Disclosure – Writing with Precision 6 minutes
Frameworks for Ethical AI Evaluation 7 minutes
Conducting a Structured AI Ethics Audit 7 minutes

4 readings Total 43 minutes

Why Documentation Defines Trust 6 minutes
Common Pitfalls in Model Documentation 6 minutes
Build Your First Model Card 25 minutes
Lessons from Real-World AI Failures 6 minutes

3 assignments Total 50 minutes

Model Documentation Review Quiz 10 minutes
Ethics Audit Checkpoint Quiz 10 minutes
AI Ethics Accountability Toolkit Project 30 minutes

2 ungraded labs Total 85 minutes

Build Your First Model Card 60 minutes
Ethics Audit Simulation – The Chatbot Review (Task-Based) 25 minutes

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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Professionals from the Industry

271 Courses 40,833 learners

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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.

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.