使用Python优化MLOps、AIOps和DevOps工作流程课程 (英文)

MLOps – AIOps – DevOps的Python编程

发布日期：2024年5月 创建者：Manifold AI Learning ® 格式：MP4 | 视频：h264, 1280×720 | 音频：AAC, 44.1 KHz, 2声道 类型：电子学习 | 语言：英语 | 时长：126讲 (17小时6分钟) | 大小：7.1 GB

使用Python优化MLOps、AIOps和DevOps工作流程

你将学到：

• 自信地将Python应用于基础设施和操作任务：使用核心原则、文件处理、模块和面向对象编程编写干净、模块化的Python源代码。
• 自动化文件相关操作：高效操作、加密和处理DevOps、MLOps和AIOps中常用的各种文件格式。
• 创建交互式命令行应用程序：使用Python构建CLI以自动化任务并简化工作流程。
• 远程高效管理Linux系统：使用Python的Fabric库进行远程执行和psutil进行系统监控。
• 创建、管理和发布Python包：将源代码组织成可重用的包并在PyPI等平台上分发。
• 使用Docker进行应用部署：了解Docker镜像创建、容器化和部署。
• 使用GitHub Actions自动化工作流程：设计和配置CI/CD管道。
• 利用AWS服务实现CI/CD工作流程：设计利用S3进行存储和EC2实例进行部署的管道。
• 为MLOps项目编写测试：使用Pytest确保MLOps的可靠性和可维护性。
• 使用源代码配置和管理基础设施：应用Pulumi的Python SDK实现基础设施即源代码（IaC）原则。
• 体验完整的MLOps管道：构建一个集成课程中学习的工具和概念的端到端MLOps解决方案。
• 设置连续监控以提高可见性：使用Prometheus和Grafana实现监控和报警。

要求：

• 不需要编程经验
• 只需一台笔记本电脑和命令行界面即可编码

课程描述：

掌握简化DevOps工作流程、实施智能MLOps管道并优化AIOps实践所需的关键Python技能。本综合课程深入探讨Python基础知识、文件自动化、命令行掌握、Linux工具、包管理、Docker、AWS上的CI/CD、基础设施自动化以及高级监控和日志记录技术。

你将发展的关键技能：

• 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干净Python源代码的最佳实践。
• 文件自动化：轻松操作文本、二进制和各种文件格式（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库高效与Linux系统交互。
• 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专家：掌握Docker容器化实现一致且可移植的部署。
• GitHub Actions自动化：为你的Python项目创建和定制GitHub Actions工作流程。
• AWS基础：设置AWS环境，使用S3存储桶，管理EC2实例，并在AWS上设计CI/CD管道。
• Pytest能力：为你的MLOps项目编写稳健且可维护的测试。
• Pulumi的基础设施即源代码：使用Pulumi的Python SDK自动化基础设施配置和管理。
• MLOps实战：参与展示完整MLOps管道的实践演示。
• 监控和日志记录：使用Prometheus和Grafana设置连续监控，获得系统的可操作见解。

适合人群：

• 希望简化DevOps流程的开发人员
• 希望提高MLOps实践的数据科学家和ML工程师
• 希望实施AIOps策略的IT专业人士
• 渴望掌握用于基础设施管理和自动化的Python的任何人

Python Programming for MLOps – AIOps – DevOps

Published 5/2024
Created by Manifold AI Learning ®
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 126 Lectures ( 17h 6m ) | Size: 7.1 GB

Optimize MLOps, AIOps, and DevOps Workflows with Python

What you’ll learn:
Apply Python confidently to infrastructure and operations tasks: Write clean, modular Python code using core principles, file handling, modules, and OOP.
Automate file-related operations: Efficiently manipulate, encrypt, and work with various file formats commonly used in DevOps, MLOps, and AIOps.
Create interactive command-line applications: Build CLIs with Python to automate tasks and streamline workflows.
Effectively manage Linux systems remotely: Use Python’s Fabric library for remote execution and psutil for system monitoring
Create, manage, and publish Python packages: Organize code into reusable packages and distribute them on platforms like PyPI.
Utilize Docker for application deployments: Understand Docker image creation, containerization, and deployment.
Automate workflows with GitHub Actions: Design and configure CI/CD pipelines using GitHub Actions.
Implement CI/CD workflows utilizing AWS services: Design pipelines that leverage S3 for storage and EC2 instances for deployment.
Write tests specifically for MLOps projects: Ensure MLOps reliability and maintainability using Pytest.
Provision and manage infrastructure using code: Apply Infrastructure as Code (IaC) principles with Pulumi’s Python SDK.
Experience a complete MLOps pipeline: Build an end-to-end MLOps solution integrating tools and concepts learned throughout the course.
Set up continuous monitoring for improved visibility: Implement monitoring and alerting using Prometheus and Grafana.

Requirements:
No Programming Experience is needed
Just a Laptop and CLI to code

Description:
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

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