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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mlops-machine-learning-operations-for-beginners/
课程评论：没有评论
**课程名称：** MLOps，面向初学者的机器学习运维 **课程概述：** 本课程专注于机器学习运维（MLOps）。近年来，机器学习（ML）和人工智能（AI）已成为热门话题，涌现了许多技术和算法，它们在解决商业问题和为公司创造价值方面被证明是有效的，例如欺诈检测、推荐系统和自动驾驶汽车等。 然而，许多已开发的机器学习模型并未投入实际生产。这其中一个主要原因是：机器学习模型并非传统的软件。适用于传统软件的现有框架和方法论在面对机器学习模型时被证明是不够的。因此，需要引入新的范式和概念来应对机器学习算法的特殊性。 本课程面向数据专业人士（数据科学家、数据工程师、机器学习工程师和软件工程师），以及任何希望了解机器学习模型从实验到生产的整个生命周期的人。我们将探讨将机器学习模型投入生产的最佳实践和推荐方法。这将使我们能够了解如何利用MLOps的力量大规模部署机器学习。最后，由于部署模型与工具密切相关，我们将探讨在采用MLOps最佳实践时如何选择合适的工具栈。 祝您学习旅途愉快！
This course is about Machine Learning Operations.Machine Learning and Artificial Intelligence have became a hot topic in recent years. Numerous techniques and algorithms were developed and proved their efficiencies in addressing business issues and bringing value to companies. Take fraud detection, recommendation systems or autonomous vehicles, etc. as examples.However, most of the developed machine learning models do not go to production! Among others, this is due to one major reason: Machine Learning models are not classical software. The existing frameworks and methodologies that work for classical software proved to be inadequate with Machine Learning models. Hence, new paradigms and concepts should be brought to handle the specificities of Machine Learning Algorithms.This course is addressed to Data professionals (Data Scientists, Data Engineers, Machine Learning Engineers and Software Engineers) as well as to everyone who want to understand the lifecycle of a Machine Learning model from experimentation to production. In this course, wa re going to see the best practices and recommended ways to put machine learning models into production. This will allow us also to see how we can leverage the power of MLOps to deploy Machine Learning at scale. Finally, as deploying models is about tooling, we are going to have a look on how to choose its perfect stack when adopting Machine Learning Operations best practices.Wish you a nice journey!
