courseraАнализ данных

Production AI Model Development and Ethics

Курс от Coursera

Средний≈ 11.8 чАнглийский

О курсе

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.

Навыки, которые вы освоите

Model Deployment Feature Engineering Technical Documentation Responsible AI Model Evaluation Model Training Data Pipelines MLOps (Machine Learning Operations)Data Preprocessing Deep Learning Model Optimization PyTorch (Machine Learning Library)Scikit Learn (Machine Learning Library)Data Ethics Software Documentation

Программа курса

3 модулей · 43 учебных материалов

01Engineer Features and Evaluate Models for Production10 материалов

Welcome! Let Us Set Your Production ML GoalsDIALOGUE The What and How of Scikit-learn PipelinesЧтение How to Build a ColumnTransformer: Step-by-StepВидео Build a Pipeline for Churn Prediction

Учитесь у экспертов

Professionals from the Industry

Преподаватель курса