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所在平台: Udemy |
课程主页: https://www.udemy.com/course/nlp-with-bert-in-python/
课程评论：没有评论
课程名称：2025 在生产中部署机器学习模型与 FastAPI 和 Docker 课程概述： 欢迎参加生产级机器学习模型部署课程，本课程结合了 FastAPI、AWS、Docker 和 NGINX 的强大力量！本课程旨在帮助数据科学家、机器学习工程师和云计算从业者将模型从开发转向生产。您将学习如何在真实环境中部署、扩展和管理机器学习模型，确保其具备强大性、可扩展性和安全性。 您将学习： 1. 使用 FastAPI 精简机器学习操作：掌握通过 FastAPI 部署机器学习模型的技巧，构建稳健的 RESTful API，实现快速高效的模型推断，确保您的 ML 解决方案既可访问又可扩展。 2. 利用 AWS 实现可扩展部署：掌握如何使用 AWS 服务（如 EC2、S3、ECR 和 Fargate）在云中部署和管理机器学习模型，获取使用 Boto3 自动化部署的实战经验，确保模型的安全、可靠和高效。 3. 使用 Docker 进行应用容器化：学习如何通过 Docker 将机器学习应用容器化，确保模型在不同环境中（从开发到生产）都能一致运行。 4. 构建和部署端到端 ML 流水线：掌握机器学习运维的复杂性，构建端到端的机器学习流水线，了解数据管理、模型监控和 A/B 测试等，确保模型在生命周期的每个阶段都能表现最佳。 5. 使用 Boto3 自动化部署：通过 Python 和 Boto3 自动化您的 ML 模型的部署，从启动 EC2 实例到管理 S3 存储，简化云操作，使部署更快速有效。 6. 使用 NGINX 扩展 ML 模型：学习如何使用 NGINX 和 Docker-Compose 扩展 ML 应用，确保生产环境中的高可用性和性能。 7. 使用 AWS Fargate 部署无服务器 ML 模型：深入了解如何利用 AWS Fargate 进行无服务器部署，学习如何使用 AWS ECR 和 ECS 打包、部署和管理可扩展的无服务器 ML 应用。 8. 真实世界 ML 应用案例：将所学知识应用于真实情境中，部署情感分析、灾难推文分类和人体姿态估计模型，利用最前沿的变换器和计算机视觉技术，获得实现人工智能的实践经验。 9. 使用 Streamlit 部署互动 ML 应用：利用 Streamlit 创建和部署交互式网页应用，将 FastAPI 驱动的模型与用户友好的界面集成，使您的 ML 解决方案对非技术用户也可用。 10. 监控和优化生产 ML 模型：实施负载测试、监控和性能优化技术，确保您的模型在生产环境中保持可靠和高效。 课程价值： 在当今快速发展的技术环境中，将机器学习模型部署到生产中的能力是一项极具需求的技能。本课程将 FastAPI、AWS、Docker、NGINX 和 Streamlit 等最新技术结合在一起，为您提供强大的学习体验。无论您是希望提升职业生涯还是增强技能，这个课程都将为您提供自信地部署、扩展和管理生产级机器学习模型所需的一切知识。 完成本课程后，您将具备在真实环境中部署既有效又可扩展、安全的机器学习模型的专业知识。加入我们，开启您在机器学习之旅的下一步！
Welcome to Production-Grade ML Model Deployment with FastAPI, AWS, Docker, and NGINX!Unlock the power of seamless ML model deployment with our comprehensive course, Production-Grade ML Model Deployment with FastAPI, AWS, Docker, and NGINX. This course is designed for data scientists, machine learning engineers, and cloud practitioners who are ready to take their models from development to production. You'll gain the skills needed to deploy, scale, and manage your machine learning models in real-world environments, ensuring they are robust, scalable, and secure.What You Will Learn:Streamline ML Operations with FastAPI: Master the art of serving machine learning models using FastAPI, one of the fastest-growing web frameworks. Learn to build robust RESTful APIs that facilitate quick and efficient model inference, ensuring your ML solutions are both accessible and scalable.Harness the Power of AWS for Scalable Deployments: Leverage AWS services like EC2, S3, ECR, and Fargate to deploy and manage your ML models in the cloud. Gain hands-on experience automating deployments with Boto3, integrating models with AWS infrastructure, and ensuring they are secure, reliable, and cost-efficient.Containerize Your Applications with Docker: Discover the flexibility of Docker to containerize your ML applications. Learn how to build, deploy, and manage Docker containers, ensuring your models run consistently across different environments, from development to production.Build and Deploy End-to-End ML Pipelines: Understand the intricacies of ML Ops by constructing end-to-end machine learning pipelines. Explore data management, model monitoring, A/B testing, and more, ensuring your models perform optimally at every stage of the lifecycle.Automate Deployments with Boto3: Automate the deployment of your ML models using Python and Boto3. From launching EC2 instances to managing S3 buckets, streamline cloud operations, making your deployments faster and more efficient.Scale ML Models with NGINX: Learn to use NGINX with Docker-Compose to scale your ML applications across multiple instances, ensuring high availability and performance in production.Deploy Serverless ML Models with AWS Fargate: Dive into serverless deployment using AWS Fargate, and learn how to package, deploy, and manage ML models with AWS ECR and ECS for scalable, serverless applications.Real-World ML Use Cases: Apply your knowledge to real-world scenarios by deploying models for sentiment analysis, disaster tweet classification, and human pose estimation. Using cutting-edge transformers and computer vision techniques, you'll gain practical experience in bringing AI to life.Deploy Interactive ML Applications with Streamlit: Create and deploy interactive web applications using Streamlit. Integrate your FastAPI-powered models into user-friendly interfaces, making your ML solutions accessible to non-technical users.Monitor and Optimize Production ML Models: Implement load testing, monitoring, and performance optimization techniques to ensure your models remain reliable and efficient in production environments.Why This Course?In today's fast-paced tech landscape, the ability to deploy machine learning models into production is a highly sought-after skill. This course combines the latest technologies-FastAPI, AWS, Docker, NGINX, and Streamlit-into one powerful learning journey. Whether you're looking to advance your career or enhance your skill set, this course provides everything you need to deploy, scale, and manage production-grade ML models with confidence.By the end of this course, you'll have the expertise to deploy machine learning models that are not only effective but also scalable, secure, and ready for production in real-world environments. Join us and take the next step in your machine-learning journey!
