图 书 馆
使用TensorFlow Extended（TFX）迈向ML工程Towards ML Engineering with TensorFlow Extended (TFX) |
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| 课程网址: | http://videolectures.net/kdd2019_katsiapis_tensorflow_extended/ |
| 主讲教师: | Konstantinos Katsiapis |
| 开课单位: | 谷歌公司 |
| 开课时间: | 2020-03-02 |
| 课程语种: | 英语 |
| 中文简介: |  在过去的5年中，软件工程学科已经发展到成熟水平。这种成熟实际上既是福也是必需，因为现代世界在很大程度上取决于它。 同时，在过去的20年中，机器学习（ML）的受欢迎程度一直在稳定增长。 ，在过去的十年中，机器学习已越来越多地用于实验和生产工作负载。机器学习已成为我们生活中不可或缺的一部分，可以为广泛使用的应用程序和产品提供动力，这一点已不再罕见。就像软件工程的情况一样，机器学习技术的使用激增，使机器学习学科从“编码”演变为“工程”。 Gus Katsiapis提供了从使用的角度出发的观点并建立了端到端的ML平台，并共享集体的知识和经验，在Google进行了十多年的应用ML处理。我们希望这有助于为ML Engineering铺平道路。 Kevin Haas概述了TensorFlow Extended（TFX），它是TensorFlow的端到端机器学习平台，可为所有Alphabet（超越）。 TFX帮助有效地管理端到端的培训和生产工作流程，包括模型管理，版本控制和服务，从而帮助人们实现ML Engineering的各个方面。 这是与Kevin Haas的联合演讲。 |
| 课程简介: | The discipline of Software Engineering has evolved over the past 5+ decades to good levels of maturity. This maturity is in fact both a blessing and a necessity, since the modern world largely depends on it. At the same time, the popularity of Machine Learning (ML) has been steadily increasing over the past 2+ decades, and over the last decade ML is being increasingly used for both experimentation and production workloads. It is no longer uncommon for ML to power widely used applications and products that are integral parts of our life. Much like what was the case for Software Engineering, the proliferation of use of ML technology necessitates the evolution of the ML discipline from “Coding” to “Engineering”. Gus Katsiapis offers a view from the trenches of using and building end-to-end ML platforms, and shares collective knowledge and experience, gothered over more than a decade of applied ML at Google. We hope this helps pave the way towards a world of ML Engineering. Kevin Haas offers an overview of TensorFlow Extended (TFX), the end-to-end machine learning platform for TensorFlow that powers products across all of Alphabet (and beyond). TFX helps effectively manage the end-to-end training and production workflow including model management, versioning, and serving, thereby helping one realize aspects of ML Engineering. This is a joint talk with Kevin Haas. |
| 关 键 词: | 机器学习; 软件工程学科; 谷歌 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-12：cjy |
| 最后编审: | 2021-01-31：nkq |
| 阅读次数: | 66 |