What is production-ready model development in this course?

In this course, production-ready model development means turning a model from a one-time experiment into a repeatable, dependable workflow. The emphasis is on consistent data preparation, careful evaluation, stable training behavior, and clear ethical documentation rather than just getting a model to work once.

When would you use a production-ready model development workflow?

You would use it when a model needs to be reused, compared, maintained, or reviewed beyond an initial experiment. The course treats it as the right approach when training, evaluation, and documentation all need to stay consistent as work moves toward real use.

How does production-ready model development fit into a broader machine learning workflow?

It sits in the build-and-test phase between having a modeling idea and relying on that model in a real setting. In the course, it connects data preparation, experiment review, training diagnostics, and responsible documentation into one repeatable process.

How is production-ready model development different from one-off model experimentation?

One-off experimentation is mainly about proving that a model can work, while production-ready development is about making the whole workflow consistent, inspectable, and maintainable. This course focuses on linking the steps together so you can judge model quality, monitor training behavior, and document limitations instead of treating each step as a separate task.

Do you need any prerequisites before learning production-ready model development?

A basic understanding of machine learning is helpful, especially around training models and reading evaluation results. Because the course is intermediate, it assumes you are ready to focus on making model work reproducible, stable, and responsibly documented rather than learning machine learning from scratch.

What tools, platforms, or methods are used in this course?

The course uses scikit-learn for repeatable feature pipelines and PyTorch-based tools for training diagnostics and tuning. It also introduces model cards and structured ethics audits as part of responsible AI development.

What specific tasks will you practice or complete in this course?

You will build repeatable data-preparation workflows, evaluate and compare model runs, diagnose unstable training, and add controls that help save the best-performing model state. You will also write model documentation and carry out ethics-focused reviews so the workflow is not only usable, but clearly scoped and accountable.

Production AI Model Development and Ethics

Ce cours n'est pas disponible en Français (France)

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Production AI Model Development and Ethics

Ce cours fait partie de Certificat Professionnel LLM Engineering That Works: Prompting, Tuning, and Retrieval

Instructeur : Professionals from the Industry

Inclus avec

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Data Pipelines
Catégorie : Model Training
Catégorie : Deep Learning
Catégorie : Model Optimization
Catégorie : Feature Engineering
Catégorie : Software Documentation
Catégorie : Technical Documentation
Catégorie : MLOps (Machine Learning Operations)
Catégorie : Data Ethics
Catégorie : Data Preprocessing
Catégorie : Model Evaluation
Catégorie : Responsible AI

Outils que vous découvrirez

Catégorie : Model Deployment
Catégorie : PyTorch (Machine Learning Library)
Catégorie : Scikit Learn (Machine Learning Library)

Détails à connaître

Certificat partageable

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mars 2026

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Ce cours fait partie de la Certificat Professionnel LLM Engineering That Works: Prompting, Tuning, and Retrieval

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Il y a 3 modules dans ce cours

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.

Détails du module

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.

Inclus

2 vidéos2 lectures2 devoirs2 laboratoires non notés

2 vidéosTotal 11 minutes

How to Build a ColumnTransformer: Step-by-Step7 minutes
Why a High Accuracy Score Can Be a Lie4 minutes

2 lecturesTotal 14 minutes

The What and How of Scikit-learn Pipelines7 minutes
From Evaluation to Recommendation7 minutes

2 devoirsTotal 60 minutes

Submit Your Feature Engineering and Evaluation Report30 minutes
AI Graded Open-Ended Questions30 minutes

2 laboratoires non notésTotal 82 minutes

Build a Pipeline for Churn Prediction22 minutes
How to Diagnose Overfitting with TensorBoard60 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.

Inclus

4 vidéos3 lectures5 devoirs2 laboratoires non notés

4 vidéosTotal 26 minutes

Building Your First LightningModule6 minutes
Implementing Callbacks in the Trainer7 minutes
When Training Goes Wrong: The Exploding Gradient7 minutes
Monitoring Gradients with a Custom Callback6 minutes

3 lecturesTotal 15 minutes

The Core Components: LightningModule and Trainer5 minutes
What are Callbacks? EarlyStopping and ModelCheckpointing5 minutes
What to Look For: Diagnosing Instability with Gradients5 minutes

5 devoirsTotal 60 minutes

Final Project: Fine-Tune, Diagnose, and Deploy30 minutes
Hands-On Learning: Refactoring Steps for a BERT LightningModule 15 minutes
Knowledge Check: Lightning Components5 minutes
Knowledge Check: Callback Configuration5 minutes
Knowledge Check: Diagnostic Scenarios5 minutes

2 laboratoires non notésTotal 120 minutes

Hands-On: Implement Early Stopping and Cloud Checkpointing60 minutes
Hands-On: Build and Use a Gradient Monitoring Callback60 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.

Inclus

4 vidéos4 lectures3 devoirs2 laboratoires non notés

4 vidéosTotal 28 minutes

Anatomy of a Model Card7 minutes
From Data to Disclosure – Writing with Precision6 minutes
Frameworks for Ethical AI Evaluation7 minutes
Conducting a Structured AI Ethics Audit7 minutes

4 lecturesTotal 43 minutes

Why Documentation Defines Trust6 minutes
Common Pitfalls in Model Documentation6 minutes
Build Your First Model Card25 minutes
Lessons from Real-World AI Failures6 minutes

3 devoirsTotal 50 minutes

AI Ethics Accountability Toolkit Project30 minutes
Model Documentation Review Quiz10 minutes
Ethics Audit Checkpoint Quiz10 minutes

2 laboratoires non notésTotal 85 minutes

Build Your First Model Card60 minutes
Ethics Audit Simulation – The Chatbot Review (Task-Based)25 minutes

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