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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-programming-for-mlops-aiops-devops/
课程评论：没有评论
课程名称：Python编程在MLOps中的应用 - 生产环境 - 2025 课程简介：此全面课程旨在帮助您掌握精简DevOps工作流程、实施智能MLOps管道和优化AIOps实践所需的基本Python技能。课程内容涵盖Python基础、文件自动化、命令行操作、Linux工具、包管理、Docker、AWS的CI/CD基础设施自动化，以及高级监控和日志记录技术。 您将掌握的关键技能： - **Python基础**：深入理解变量、数据类型、控制结构、函数、面向对象编程以及编写整洁Python代码的最佳实践。 - **文件自动化**：轻松处理文本、二进制和多种MLOps、AIOps及DevOps项目中使用的文件格式（如CSV、JSON等）。学习安全文件处理的加密策略。 - **命令行能力**：使用如argparse、Click和fire等Python库构建命令行接口并自动化任务。 - **Linux集成**：使用Python的Fabric和psutil库有效地与Linux系统进行交互。 - **包管理**：学习创建、管理和发布自己的Python包，以简化工作流程。 - **Docker专业知识**：掌握Docker容器化技术，实现一致且可移植的部署。 - **GitHub Actions自动化**：为您的Python项目创建和定制GitHub Actions工作流。 - **AWS基础**：设置AWS环境，使用S3存储桶，管理EC2实例，并设计AWS上的CI/CD管道。 - **Pytest强大能力**：使用Pytest为您的MLOps项目编写稳健且易维护的测试。 - **基础设施即代码**：使用Pulumi的Python SDK自动化基础设施的配置和管理。 - **MLOps实战**：参与动手演示，展示完整的MLOps管道。 - **监控与日志记录**：使用Prometheus和Grafana设置持续监控，以获得针对系统的可操作洞察。 适合对象： - 有意简化DevOps流程的开发人员 - 希望提升MLOps实践的数据科学家和机器学习工程师 - 想要实施AIOps策略的IT专业人员 - 渴望掌握Python基础设施管理与自动化的任何人 通过此课程，您将奠定坚实的Python基础，并能够有效地在MLOps和DevOps环境中应用所学技能。
Master the essential Python skills you need to streamline DevOps workflows, implement intelligent MLOps pipelines, and optimize AIOps practices. This comprehensive course dives into Python fundamentals, file automation, command-line mastery, Linux utilities, package management, Docker, CI/CD with AWS, infrastructure automation, and even advanced monitoring and logging techniques.Key Skills You'll Develop:Python Foundations: Get a robust understanding of variables, data types, control structures, functions, object-oriented programming, and best practices for clean Python code.File Automation: Effortlessly manipulate text, binary, and various file formats (like CSV, JSON, and more) used in MLOps, AIOps, and DevOps projects. Learn encryption strategies for secure file handling.Command-Line Power: Build command-line interfaces and automate tasks with Python libraries like argparse, Click, and fire.Linux Integration: Interact with Linux systems effectively using Python's Fabric and psutil libraries.Package Management: Learn to create, manage, and publish your own Python packages to streamline your workflows.Docker Expertise: Master Docker containerization for consistent and portable deployments.GitHub Actions Automation: Create and customize GitHub Actions workflows for your Python projects.AWS Essentials: Set up your AWS environment, work with S3 buckets, manage EC2 instances, and design CI/CD pipelines on AWS.Pytest Power: Write robust and maintainable tests for your MLOps projects using Pytest.Infrastructure as Code with Pulumi: Automate infrastructure provisioning and management using Pulumi's Python SDK.MLOps in Action: Participate in a hands-on demo showcasing a complete MLOps pipeline.Monitoring & Logging: Set up continuous monitoring with Prometheus and Grafana for actionable insights into your systems.Who This Course Is For:Developers interested in streamlining DevOps processesData scientists and ML engineers looking to enhance MLOps practicesIT professionals wanting to implement AIOps strategiesAnyone eager to master Python for infrastructure management and automation
