К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
Real-World Applications & Model Deployment in Java · LearnSpace
Назад в каталог
courseraАнализ данных

Real-World Applications & Model Deployment in Java

Курс от Board Infinity
Средний≈ 8.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Course Description: Take your machine learning skills to the next level by learning how to deploy real-world ML applications using Java. In this hands-on course, you’ll use tools like Spring Boot, Jenkins, GitHub Actions, and RL4J to integrate, automate, and monitor ML systems in enterprise environments—no advanced ML background required. In the first module, you’ll explore how machine learning is applied in industries like banking and e-commerce. You’ll learn to build and expose ML models through Spring Boot REST APIs and automate deployment workflows using Jenkins and GitHub Actions. The second module introduces advanced concepts like reinforcement learning, federated learning, and responsible AI. You'll explore how to build ethical, fair, and secure AI systems. In the final module, you’ll apply your learning in a capstone project—designing, deploying, and monitoring a complete ML pipeline while exploring career opportunities in MLOps and AI engineering. Learning Objectives: -Deploy ML models in Java applications using Spring Boot, REST APIs, and edge deployment tools. -Automate ML pipelines with MLOps tools like Jenkins and GitHub Actions. -Apply reinforcement learning, federated learning, and responsible AI practices in enterprise contexts. Target Audience: This course is ideal for: -Experienced Java developers and machine learning practitioners ready to deploy ML in production. -Engineers working on enterprise software who need to integrate or scale ML capabilities. -DevOps or MLOps professionals seeking to automate ML workflows in Java-based stacks. -Professionals interested in responsible AI, edge computing, and advanced ML concepts like reinforcement or federated learning. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.

Навыки, которые вы освоите

Model DeploymentJavaSpring FrameworkReinforcement LearningSpring BootFederated LearningContinuous DeploymentJava ProgrammingArtificial Intelligence and Machine Learning (AI/ML)Data EthicsApplied Machine LearningArtificial IntelligenceResponsible AIMLOps (Machine Learning Operations)CI/CDMachine LearningAI SecurityJenkins

Программа курса

3 модулей · 40 учебных материалов

01Enterprise Applications of Machine Learning18 материалов

Introduction to the Course

Course Welcome Video!Видео

ML in Enterprise Java – Case Studies

Fraud Detection in Banking Using Java-Based MLВидеоBuilding a Recommendation System for E-CommerceВидеоRead More About Applied AI in Java: Fraud Detection & E-Commerce Recommendation SystemsЧтение

Учитесь у экспертов

Board Infinity

Instructor

Real-World Applications & Model Deployment in Java
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 8.4 ч

3 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Казахский, Венгерский

Часть программы вашего университета
Java in the Real World: Exploring ML Use Cases in Enterprise SystemsDIALOGUE
Practice Quiz: ML in Enterprise Java – Case StudiesЗадание

Integrating ML into Java Applications

SPRING boot REST API for integrating ML into JVM (Spring Boot API)ВидеоModel Servers and Embedded ML on Edge DevicesВидеоRead More About Intelligent Java Solutions: Spring Boot, AI Integration & Edge ML in PracticeЧтениеPractice Quiz: Integrating ML into Java ApplicationsЗадание

MLOps and Pipeline Automation

Monitoring, Retraining, and Maintaining ML ModelsВидеоCI/CD for ML Pipelines Using Jenkins and GitHub ActionsВидеоAutomating Retraining and Monitoring in ProductionВидеоRead More About Production-Ready Machine Learning: Monitoring, CI/CD & Lifecycle AutomationЧтениеEnterprise ML Applications, Integration, and MLOpsDIALOGUEPractice Quiz: MLOps and Pipeline AutomationЗаданиеGraded Quiz: Enterprise Applications of Machine LearningЗаданиеQuick Course Check-InPLUGIN
02Advanced Topics and Emerging Trends12 материалов

Advanced Machine Learning Techniques

Reinforcement Learning Basics and RL4J in JavaВидеоIntroduction to Federated Learning & Graph based ML ConceptsВидеоRead More About Reinforcement & Federated Learning: Foundations of Intelligent, Privacy-Preserving AIЧтениеPractice Quiz: Advanced Machine Learning TechniquesЗадание

AI Ethics and Responsible AI in Enterprise

Understanding AI Ethics, Principles & Critical IssuesВидеоAddressing Bias & Making AI Decisions UnderstandableВидеоApplying Responsible AI in FinanceВидеоTools For Fairness in AI SystemsВидеоRead More About Ethical AI & Fairness: Principles, Bias Challenges, and Responsible DeploymentЧтениеReinforcement Learning, Federated AI, and Responsible Enterprise AIDIALOGUEPractice Quiz: AI Ethics and Responsible AI in EnterpriseЗаданиеGraded Quiz: Advanced Topics and Emerging TrendsЗадание
03Optional Extension or Workshops10 материалов

Capstone Project – End-to-End ML Solution in Java

Project Overview – Predicting Equipment FailuresВидеоModel Deployment & Next StepsВидеоHands on Equipment Failure Prediction Problem of CapstoneВидеоRead More About End-to-End Machine Learning Pipeline: From Data Ingestion to Model DeploymentЧтениеPractice Quiz: Capstone Project – End-to-End ML Solution in JavaЗадание

Career and Next Steps

Learning Paths and Real world Job rolesВидеоCapstone Project, Deployment, and Career Pathways in ML with JavaDIALOGUEPractice Quiz: Career and Next StepsЗаданиеGraded Quiz: Optional Extension or WorkshopsЗаданиеCourse Closure!Видео