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 stars70.30%
- 4 stars21.59%
- 3 stars5.83%
- 2 stars1.51%
- 1 star0.75%
來自PRODUCTION MACHINE LEARNING SYSTEMS的熱門評論
very good information. Lot of unknown facts in ML are brought up in the course.
Course is a bit outdated. E.g., KubeFlow section and references to ML Engine.
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
Very practical which was nice. Thank you for adding the Quicklabs that helped a lot.
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