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所在平台: Udemy |
课程主页: https://www.udemy.com/course/fastapi-banking-with-ai-ml-fraud-detection/
课程评论：没有评论
课程名称：完整的FASTAPI银行系统：带AI/ML的银行欺诈检测 课程概述：欢迎参加这门全面的课程，学习如何利用FastAPI构建一个银行API，并实现一个基于AI和机器学习的交易分析及欺诈检测系统。本课程不仅限于基本的API开发，还将向您展示如何设计一个生产就绪、安全且可扩展的完整银行系统。 课程独特之处： - 学习使用FastAPI和SQLModel构建真实的银行系统 - 实现基于MLflow和scikit-learn的AI/ML欺诈检测 - 精通Docker的容器化 - 理解Traefik的反向代理和负载均衡 - 使用Celery、Redis和RabbitMQ处理高交易量 - 采用行业标准的身份验证方法保护API 您将学习到： - 设计一个坚固的银行API架构，运用领域驱动设计原则 - 实现JWT、OTP验证和速率限制的安全用户身份验证 - 创建带有货币转换和欺诈检测的交易处理系统 - 构建实时交易风险分析的机器学习管道 - 使用Docker Compose进行部署，并通过Traefik管理流量 - 使用异步Celery工作者扩展应用程序 - 通过Loguru进行全面的系统监控 - 使用MLflow训练、评估和部署机器学习模型 - 运用SQLModel和Alembic进行PostgreSQL数据管理 项目的关键特性： - 核心银行功能：账户创建、转账、存款、取款、账单 - 虚拟卡管理：卡片创建、激活、阻止和充值 - 用户管理：个人资料、近亲信息、KYC实施 - AI/ML驱动的欺诈检测：基于机器学习的交易分析和欺诈检测 - 背景处理：电子邮件通知、PDF生成和机器学习训练 - 高级部署：容器编排、反向代理和高可用性 - 机器学习运维：模型训练、评估、部署和监控 这门课程非常适合： - 至少有1年经验的后端开发人员，想要构建安全的金融科技解决方案 - 计划设计金融科技解决方案的技术负责人 课程结束时，您将构建一个具备AI能力的生产就绪银行系统，您可以在个人作品集中展示或者实际应用于项目中。 您将掌握的技术： - FastAPI和SQLModel：构建高性能、类型安全的API - Docker和Traefik：实现容器化和智能请求路由 - Celery和RabbitMQ：用于分布式任务处理 - PostgreSQL和Alembic：强大的数据存储及模式迁移 - Scikit-learn：用于机器学习 - MLflow：管理机器学习生命周期 - Pydantic V2：数据验证和设置管理 - JWT和OTP：确保安全的身份验证流程 - Cloudinary：处理图像上传 - 速率限制：保护API免受滥用 不再是基础教程，让我们一起构建真实的项目！
Welcome to this comprehensive course on building a banking API with FastAPI with an AI-powered/machine learning transaction analysis and fraud detection system. This course goes beyond basic API development to show you how to architect a complete banking system that's production-ready, secure, and scalable.What Makes This Course Unique:Learn to build a real-world banking system with FastAPI and SQLModelImplement AI/ML-powered fraud detection using MLflow and scikit-learnMaster containerization with Docker Master reverse proxying and load balancing with TraefikHandle high-volume transactions with Celery, Redis, and RabbitMQSecure your API with industry-standard authentication practicesYou'll Learn How To:✓ Design a robust banking API architecture with domain-driven design principles✓ Implement secure user authentication with JWT, OTP verification, and rate limiting✓ Create transaction processing with currency conversions and fraud detection✓ Build a machine learning pipeline for real-time transaction risk analysis✓ Deploy with Docker Compose and manage traffic with Traefik✓ Scale your application using asynchronous Celery workers✓ Monitor your system with comprehensive logging using Loguru✓ Train, evaluate, and deploy ML models with MLflow✓ Work with PostgreSQL using SQLModel and Alembic for migrationsKey Features in This Project:Core Banking Functionality: Account creation, transfers, deposits, withdrawals, statementsVirtual Card Management: Card creation, activation, blocking, and top-upsUser Management: Profiles, Next of Kin information, KYC implementationAI/ML-Powered Fraud Detection: ML-based transaction analysis and fraud detectionBackground Processing: Email notifications, PDF generation, and ML trainingAdvanced Deployment: Container orchestration, reverse proxying, and high availabilityML Ops: Model training, evaluation, deployment, and monitoringThis course is perfect For:• Backend developers with at least 1 year of experience, looking to build secure fintech solutions.• Tech leads planning to architect fintech solutions.By the end of this course, you'll have built a production-ready banking system with AI capabilities that you can showcase in your portfolio or implement in real-world projects.Technologies You'll Master:FastAPI & SQLModel: For building high-performance, type-safe APIsDocker & Traefik: For containerization and intelligent request routingCelery & RabbitMQ: For distributed task processingPostgreSQL & Alembic: For robust data storage and schema migrationsScikit-learn: For machine learning.MLflow: For managing the machine learning lifecyclePydantic V2: For data validation and settings managementJWT & OTP: For secure authentication flowsCloudinary: For handling image uploadsRate Limiting: For API protection against abuseNo more basic tutorials - let's build something real!
