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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deploying-python-applications-on-google-cloud-platform/
课程评论：没有评论
课程名称: 在 Google Cloud Platform 上部署 Python 应用程序 课程概述: 学习如何在生产环境中实现机器学习模型是希望超越理论分析并创造实质商业影响的数据科学家所需的重要技能。尽管构建模型至关重要，但在部署阶段，这些解决方案才能真正显现价值，变得可供最终用户使用，并与现实世界系统集成。掌握这一阶段使数据科学家能够确保解决方案的可扩展性，监控动态环境中的性能，并与开发和运营团队进行有效协作。同时，了解从训练到云部署的完整生命周期，也增强了专业相关性，使数据科学家能够在概念到运营的过程中成为具有战略意义的参与者。 该入门课程专为希望学习如何在 Google Cloud Platform (GCP) 上部署其首个 AI 应用的开发者、机器学习爱好者和数据专业人士而设计。通过实践性的学习，你将从训练卷积神经网络 (CNN) 进行图像分类开始，逐步学习如何将模型部署到可扩展的云服务中。课程包括对关键 GCP 服务的介绍，如 Google Compute Engine (GCE)、App Engine (GAE)、Kubernetes Engine (GKE)、Cloud Run 和 Cloud Functions，帮助你比较并选择最适合项目的选项。 在课程的第一阶段，你将设置本地环境：导入库（如 TensorFlow/Keras），训练和评估你的 CNN 模型，并创建一个简单的 Python 应用程序来与训练好的模型集成。接下来，你将学习如何配置 GCP，并将应用部署到不同的服务。 该课程适合云计算初学者及希望将机器学习模型投入生产的专业人士。到课程结束时，你将成功在云端部署一个用于图像分类的功能性网络应用，全面掌握从模型训练到在谷歌专业服务上部署的整个开发周期。
Learning to implement machine learning models in production is a critical skill for data scientists who want to move beyond theoretical analysis and create practical business impact. While building models is essential, it is during deployment that these solutions come to life, becoming accessible to end users and integrating into real-world systems. Mastering this phase allows data scientists to ensure the scalability of their solutions, monitor performance in dynamic environments, and collaborate effectively with development and operations teams. Additionally, understanding the full lifecycle-from training to cloud deployment-enhances professional relevance, positioning data scientists as strategic players capable of delivering tangible value from conception to operation.This introductory course is designed for developers, machine learning enthusiasts, and data professionals who want to learn how to deploy their first AI applications on the web using Google Cloud Platform (GCP). Through a hands-on approach, you will be guided from training a convolutional neural network (CNN) for image classification to deploying the model on scalable cloud services. The course includes an introduction to key GCP services such as Google Compute Engine (GCE), App Engine (GAE), Kubernetes Engine (GKE), Cloud Run, and Cloud Functions, enabling you to compare and choose the best option for your project.In the first stage, you will set up your local environment: import libraries (like TensorFlow/Keras), train and evaluate your CNN model, and create a simple Python application to integrate with the trained model. Next, you will learn how to configure GCP and deploy to different services.Ideal for cloud computing beginners and professionals looking to put machine learning models into production. By the end, you will have deployed a functional web application for image classification in the cloud, mastering the full development cycle-from model training to deployment on Google's professional services.
