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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deployment-of-machine-learning-models-l/
课程评论：没有评论
课程名称：机器学习模型的部署 概述：本课程专为已经构建出优秀深度学习模型的人工智能和机器学习工程师、从业者和研究人员而设计，特别适合那些有意开发应用但发现将模型部署到生产环境并非易事的学员。例如，想要开发一个使用相机传感器感知周围环境、构建地图并最终进行导航的机器人时，学员们也会发现，模型在训练机器上表现良好的背后，仍然有很长的路要走。此外，软件工程师的主要工作是构建一个有效的系统或应用，随着人工智能应用的扩展，他们常常需要将AI模型整合入自己的软件中。这可能是来自公司内部的研究团队，或是使用互联网预训练模型和API来完成任务。 本课程涵盖了所有这些部署场景，讲述从工作模型到优化部署模型的整个过程，主要聚焦于计算机视觉的部署。我们将探讨移动设备（如Android设备）、边缘计算（如树莓派等嵌入式板）的部署，以及在浏览器（如Chrome、Edge、Safari等）中运行AI模型的浏览器部署。此外，还将讨论服务器部署场景，这类场景通常出现在拥有数百万用户的高可扩展性应用和工业场合下，例如工厂中的AI视觉检测。 虽然本课程主要以实践为导向，聚焦于“如何”实施和最佳实践，但也涵盖了一些理论部分，探讨“什么”和“为什么”采用这些技术。这有时需要理解针对速度和内存优化的新型卷积操作，或掌握适用于嵌入式和边缘部署的模型压缩技术，这些在最初构建表现优秀的模型时并未涉及。
This course is for AI and ML Engineers, Practitioners and Researchers who already built an awesome Deep Learning model, and they have a great idea for an app. But they discovered that it is not straight forward to deploy their model in a production App. Another example, say you want to build a robot that uses the Camera sensor to perceive the surrounding environment, build a map of it and eventually navigate it. Here also you discover that you still have a long Journey to go after your model is already performing great on your training machine. Finally, Software Engineers, who have their primary job is to build a working system or an app, often find themselves in a situation where they need to integrate an AI model in their software, which happens a lot today with the expansion of AI applications. They might get this model from a research team in their firm or company, or even use an API or pre-trained model on the internet to do their task.We cover all those deployment scenarios, covering the journey from working trained model to an optimized deployed model. Our focus will be on CV deployment mainly. We cover Mobile deployment like on Android devices, Edge deployment on Embedded boards like Rasperry Pi, and Browser deployment where your AI model is running in the browser like Chrome, Edge, Safari or any other browser. Also, we cover server deployment scenarios, which are often found in highly scalable apps and systems with millions of users, and also in industrial scenarios like AI visual inspection in factories.While the course is mostly practical, focusing on "How" things are done and the best way of doing it, we cover also some theoretical parts about the "what" and "why" those techniques are used.This requires sometimes to understand new types of convolution operations that are optimized for speed and memory, or understanding some model compression techniques that makes them suitable for Embedded and Edge deployments, which was not in scope during building the initial model that was already performing great.
