返回到 TensorFlow Serving with Docker for Model Deployment
學生對 Coursera Project Network 提供的 TensorFlow Serving with Docker for Model Deployment 的評價和反饋
43 個評分
課程概述
This is a hands-on, guided project on deploying deep learning models using TensorFlow Serving with Docker. In this 1.5 hour long project, you will train and export TensorFlow models for text classification, learn how to deploy models with TF Serving and Docker in 90 seconds, and build simple gRPC and REST-based clients in Python for model inference.
With the worldwide adoption of machine learning and AI by organizations, it is becoming increasingly important for data scientists and machine learning engineers to know how to deploy models to production. While DevOps groups are fantastic at scaling applications, they are not the experts in ML ecosystems such as TensorFlow and PyTorch. This guided project gives learners a solid, real-world foundation of pushing your TensorFlow models from development to production in no time!
Prerequisites:
In order to successfully complete this project, you should be familiar with Python, and have prior experience with building models with Keras or TensorFlow.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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1 - TensorFlow Serving with Docker for Model Deployment 的 9 個評論（共 9 個）
創建者 Enzo G
•2020年10月18日
Introducción a tensorflow serving poderosa, muy bien explicada y con pocas líneas de código
創建者 Gabriel I P L
•2020年8月26日
Excellent!
創建者 Bryan R
•2021年4月23日
Very well structured. It took a little longer that the 1.5 hours but the time was well spent. Nice job by the instructor!
創建者 Ro H
•2021年2月20日
A fantastic introduction to TF Serving.
創建者 serdar b
•2021年1月18日
Good instructor. He explains clearly.
創建者 Kristian V
•2021年2月14日
awesome guided project
創建者 Carlos M C F
•2020年8月26日
Thank you
創建者 Igor K
•2021年8月15日
Awesome!
創建者 David W
•2020年11月10日
I wish we had spent a little more time going over some of the options on tf-server. Rarely in the real world are the simple things enough. Other than that, this was a very good summary of the process and the benefits of using tf server.
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