This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
- 5 stars70.34%
- 4 stars21.62%
- 3 stars5.90%
- 2 stars1.56%
- 1 star0.55%
来自PRODUCTION MACHINE LEARNING SYSTEMS的热门评论
Unlike pure technical courses, this one specially packs you with knowledge that you may find yourself face to. The course is really well designed and the content is crystal clear, just Awesome !
It is very good course, gives good overview over large ML systems on cloud, a lot of examples from real implementations gives good understunding about problematics in projects realisations
Excellent overview of designing real-world ML systems. Some of the labs are daunting, but the emphasis is showing you what can be achieved, rather than achieving mastery within the course.
I did not realize the many aspects to consider implementing a Production ML system. This course presents all of them and provides guidance for evaluating alternative
该课程是 Coursera 上提供的为数不多的课程之一，目前只对已购买课程或已获得助学金的学生开放。
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Last Updated: September 1, 2015