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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mastering-aiml-with-docker/
课程评论：没有评论
**[最新] 面向AI/ML和MLOps从业者的终极Docker训练营** 本课程是为AI/ML工程师量身打造的终极项目制Docker学习体验。无论您是机器学习爱好者、MLOps实践者，还是支持AI团队的DevOps专家，本课程都将教会您如何充分利用Docker的力量，提升AI/ML的开发、部署和环境一致性。 **课程亮点：** * **项目驱动学习：** 课程围绕实际项目和动手实验展开，您将通过实践学习，无需任何“废话”模块。 * **AI/ML专注：** 内容高度契合机器学习实践者的需求，而非泛泛而谈的Docker教程。 * **现代化能力：** 掌握如何在Docker环境中运行大语言模型（LLMs），学习使用Docker Model Runner和Model Context Protocol (MCP) Toolkit。 * **全能技术栈：** 涵盖FastAPI、Streamlit、Docker Compose、DevContainers等关键技术。 **您将构建的项目：** * 可复现的Jupyter + Scikit-learn开发环境 * 使用FastAPI封装的Docker化机器学习模型 * 用于实时机器学习推理的Streamlit仪表板 * 基于Docker Model Runner的LLM运行器 * 全栈Docker Compose部署（前端+模型+API） * 用于构建和推送Docker镜像的CI/CD流水线 **课程结束后，您将能够：** * 在团队之间标准化机器学习环境 * 自信地部署模型，从本地到云端 * 通过一行命令复现实验 * 解决“在我机器上可以运行”的调试难题 * 构建可移植且可扩展的机器学习开发工作流
Welcome to the ultimate project-based course on Docker for AI/ML Engineers.Whether you're a machine learning enthusiast, an MLOps practitioner, or a DevOps pro supporting AI teams - this course will teach you how to harness the full power of Docker for AI/ML development, deployment, and consistency.What's Inside?This course is built around hands-on labs and real projects. You'll learn by doing - containerizing notebooks, serving models with FastAPI, building ML dashboards, deploying multi-service stacks, and even running large language models (LLMs) using Dockerized environments.Each module is a standalone project you can reuse in your job or portfolio.What Makes This Course Different?Project-based learning: Each module has a real-world use case - no fluff.AI/ML Focused: Tailored for the needs of ML practitioners, not generic Docker tutorials.MCP & LLM Ready: Learn how to run LLMs locally with Docker Model Runner and use Docker MCP Toolkit to get started with Model Context ProtocolFastAPI, Streamlit, Compose, DevContainers - all in one course.Projects You'll BuildReproducible Jupyter + Scikit-learn dev environmentFastAPI-wrapped ML model in a Docker containerStreamlit dashboard for real-time ML inferenceLLM runner using Docker Model RunnerFull-stack Compose setup (frontend + model + API)CI/CD pipeline to build and push Docker imagesBy the end of the course, you'll be able to:Standardize your ML environments across teamsDeploy models with confidence - from laptop to cloudReproduce experiments in one line with DockerSave time debugging "it worked on my machine" issuesBuild a portable and scalable ML development workflow
