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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/devops-dataops-mlops-duke
课程评论：没有评论
课程名称：DevOps, DataOps, MLOps 课程概述：本课程旨在教授如何应用机器学习运维（MLOps）来解决实际问题。内容涵盖使用人工智能（AI）搭配编程，如GitHub Copilot，构建机器学习（ML）和AI应用的端到端解决方案。该课程适合希望从事数据科学家、软件工程师、开发者、数据分析师或其他与ML相关角色的人士。 课程大纲： 1. **MLOps介绍** 学习如何运用MLOps的基础技能来构建机器学习解决方案，并通过在Python中构建微服务来实践这些技能。 2. **基本数学和数据科学** 学习在MLOps中应用的基本数学和数据科学技能，并通过构建模拟进行实践。 3. **运维管道：DevOps、DataOps、MLOps** 学习如何构建运维管道，并通过为预训练的Hugging Face模型构建解决方案来实践这些技能。 4. **端到端MLOps和AIOps** 学习如何构建端到端的MLOps和AIOps解决方案，并通过利用OpenAI的预训练模型构建解决方案，同时使用AI配对编程工具（如GitHub Copilot）来获益。 5. **MLOps中的Rust：从Python到Rust的实用过渡** 学习如何从Python切换到一种强大高效的系统编程语言Rust。该模块涵盖Rust的多种实际应用，如命令行工具、web应用及MLOps解决方案，以及针对AWS、GCP和Azure的云计算解决方案。此外，还将学习如何为Kubernetes、Docker、无服务器架构、数据工程、数据科学和机器学习运维（MLOps）构建Rust解决方案。完成本模块后，您将对Rust的关键语法和特性有深入理解，并能够利用Rust进行GPU加速的机器学习任务。 通过本课程，学员将在多个领域获得关于MLOps的实用知识和技能，能够应对现代数据科学和软件开发的挑战。
Name:Introduction to MLOps
Description:In this module, you will learn how to apply foundational skills in MLOps to build machine learning solutions and apply it by building microservices in Python.
Name:Essential Math and Data Science
Description:In this module, you will learn how to apply essential skills in math and data science for MLOps and apply it by building simulations.
Name:Operations Pipelines: DevOps, DataOps, MLOps
Description:In this module, you will learn how to build operations pipelines and then apply these skills by building solutions for pre-trained Hugging Face models.
Name:End to End MLOps and AIOps
Description:In this module, you will learn how to build end to end MLOps and AIOps solutions and apply it by building solutions with pre-trained models from OpenAI while benefiting from using AI Pair Programming tools like GitHub Copilot.
Name:Rust for MLOps: The Practical Transition from Python to Rust
Description:In this module, you will learn how to switch from Python to Rust, a powerful and efficient systems programming language. This module will cover various practical applications of Rust, such as CLI, Web, and MLOps solutions, as well as cloud computing solutions for AWS, GCP, and Azure. You'll also learn how to build Rust solutions for Kubernetes, Docker, Serverless, Data Engineering, Data Science, and Machine Learning Operations (MLOps). By the end of this module, you will have a strong understanding of Rust's key syntax and features, and be able to leverage Rust for GPU-accelerated machine learning tasks.
Learn how to apply Machine Learning Operations (MLOps) to solve real-world problems. The course covers end-to-end solutions with Artificial Intelligence (AI) pair programming using technologies like GitHub Copilot to build solutions for machine learning (ML) and AI applications. This course is for people working (or seeking to work) as data scientists, software engineers or developers, data analysts, or other roles that use ML. By the end of the course, you will be able to use web frameworks (e
