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所在平台: Udemy |
课程主页: https://www.udemy.com/course/hands-on-mle-mlops/
课程评论：没有评论
**课程名称：** 动手实践机器学习工程与运维 (Hands-On Machine Learning Engineering & Operations) **课程概述：** 本课程旨在帮助您将概念验证 (PoCs) 和小型项目转化为可扩展的AI系统。课程旨在解决从一个有前景但无法访问的、运行在笔记本上的模型，到能够被广泛使用的、结构化的、可部署的AI系统之间的鸿沟。许多学习者在项目中会遇到代码混乱、模型重构和部署困难的问题，并难以整合碎片化的机器学习工程 (MLE) 和机器学习运维 (MLOps) 信息。本课程将引导您掌握相关的决策能力，并帮助您向他人介绍和推广您的项目。 课程将通过一个端到端的机器学习项目，运用最新的云平台技术，教授您机器学习工程与运维的关键概念。课程内容结构清晰，循序渐进，便于理解。您将受益于直观的讲座、实时编码和指导性实验，通过解决一个实际用例来学习。这个用例将成为您未来项目的宝贵参考。完成课程后，您将能够自信地编写高效的、可扩展的代码，将模型部署到本地环境之外，并以迭代的方式设计解决方案。
Transform your PoCs & small projects into scalable AI SystemsYou love to kickstart projects, but you always get stuck in the same development stage: a functional notebook - with a promising solution - that no one can access yet. The code is messy; refactoring & deploying the model seems daunting.So you rummage online and crunch through Medium tutorials to learn about Machine Learning Engineering - but you haven't been able to glue all of the information together. When it comes to making decisions between technologies and development paths, you get lost. You can't get other developers excited about your project.Time to learn about MLE & MLOPS.This training will aim to solve this by taking you through the design and engineering of an end-to-end Machine Learning project on top of the latest Cloud Platform technologies. It will cover a wide variety of concepts, structured in a way that allows you to understand the field step by step.You'll get access to intuitive Lectures, Live Coding & Guided Labs to solve a practical use case that will serve as an example you can use for any of your future projects. By the end of the course, you should be more confident in your abilities to write efficient code at scale, deploy your models outside of your local environment, an design solutions iteratively.
