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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ml-ops-beginner/
课程评论：没有评论
课程名称：ML Ops：初学者 课程概述： ML Ops在LinkedIn的新兴职业排名中位居首位，五年来增长率达到9.8倍。许多希望进入数据行业的人具备机器学习技能，然而，大部分数据科学家无法将构建的模型投入生产。这导致公司逐渐意识到模型与生产之间的鸿沟，很多机器学习模型在公司内部无法使用，最终未能惠及终端用户。因此，ML Ops工程师作为一种新角色，弥补了这一差距，使公司能够将数据科学模型投入生产，从中获得价值。 ML Ops是一个快速发展的领域，越来越多的公司开始认识到，仅有数据科学家无法充分利用机器学习模型的价值。因此，虽然很多希望进入数据行业的人专注于数据科学，但转向ML Ops将是一个不错的选择，因为这一领域同样高薪且竞争尚不激烈。 本课程从基础开始教授ML Ops，主要涉及实施和自动化机器学习系统的持续集成、持续交付和持续训练的技术。课程旨在帮助学员将机器学习创意从白板带入生产环境，学习如何将ML模型部署到云端。课程内容包括如何本地与ML模型互动，创建API（使用FastAPI和gRPC），容器化（使用Docker），以及将容器部署到AWS和GCP。在课程结束时，学员将掌握将机器学习工作流和模型投入生产所需的基础知识。 课程大纲： 1. 介绍 2. 环境搭建 3. PyTorch模型推理 4. Tensorflow模型推理 5. API概述 6. FastAPI 7. gRPC 8. 使用Docker容器化我们的API 9. 将容器部署到AWS 10. 将容器部署到GCP 11. 结论
ML Ops topped LinkedIn's Emerging Jobs ranking, with a recorded growth of 9.8 times in five years.Most individuals looking to enter the data industry possess machine learning skills. However, most data scientists are unable to put the models they build into production. As a result, companies are now starting to see a gap between models and production. Most machine learning models built in these companies are not usable, as they do not reach the end-user's hands. ML Ops engineering is a new role that bridges this gap and allows companies to productionize their data science models to get value out of them.This is a rapidly growing field, as more companies are starting to realize that data scientists alone aren't sufficient to get value out of machine learning models. It doesn't matter how highly accurate a machine learning model is if it is unusable in a production setting.Most people looking to break into the data industry tend to focus on data science. It is a good idea to shift your focus to ML Ops since it is an equally high-paying field that isn't highly saturated yet.Learn ML Ops from the ground up! ML Ops can be described as the techniques for implementing and automating continuous integration, continuous delivery, and continuous training for machine learning systems. As most of you know, the majority of ML models never see life outside of the whiteboard or Jupyter notebook. This course is the first step in changing that!Take your ML ideas from the whiteboard to production by learning how to deploy ML models to the cloud! This includes learning how to interact with ML models locally, then creating an API (FastAPI & gRPC), containerize (Docker), and then deploy (AWS & GCP). At the end of this course you will have the foundational knowledge to productionize your ML workflows and models.Course outline: 1. Introduction 2. Environment set up 3. PyTorch model inference 4. Tensorflow model inference 5. API introduction 6. FastAPI 7. gRPC 8. Containerize our APIs using Docker 9. Deploy containers to AWS 10. Deploy containers to GCP 11. Conclusion
