返回到 TensorFlow Serving with Docker for Model Deployment
学生对 Coursera Project Network 提供的 TensorFlow Serving with Docker for Model Deployment 的评价和反馈
43 个评分
课程概述
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 个评论（共 9 个）
创建者 Enzo G
•Oct 18, 2020
Introducción a tensorflow serving poderosa, muy bien explicada y con pocas líneas de código
创建者 Gabriel I P L
•Aug 26, 2020
Excellent!
创建者 Bryan R
•Apr 23, 2021
Very well structured. It took a little longer that the 1.5 hours but the time was well spent. Nice job by the instructor!
创建者 Ro H
•Feb 20, 2021
A fantastic introduction to TF Serving.
创建者 serdar b
•Jan 18, 2021
Good instructor. He explains clearly.
创建者 Kristian V
•Feb 14, 2021
awesome guided project
创建者 Carlos M C F
•Aug 26, 2020
Thank you
创建者 Igor K
•Aug 15, 2021
Awesome!
创建者 David W
•Nov 10, 2020
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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