TensorFlow Serving with Docker for Model Deployment(으)로 돌아가기
Coursera Project Network의 TensorFlow Serving with Docker for Model Deployment 학습자 리뷰 및 피드백
42개의 평가
강좌 소개
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.
최상위 리뷰
필터링 기준:
TensorFlow Serving with Docker for Model Deployment의 9개 리뷰 중 1~9
교육 기관: Enzo G
•2020년 10월 18일
Introducción a tensorflow serving poderosa, muy bien explicada y con pocas líneas de código
교육 기관: Gabriel I P L
•2020년 8월 26일
Excellent!
교육 기관: Bryan R
•2021년 4월 23일
Very well structured. It took a little longer that the 1.5 hours but the time was well spent. Nice job by the instructor!
교육 기관: Ro H
•2021년 2월 20일
A fantastic introduction to TF Serving.
교육 기관: serdar b
•2021년 1월 18일
Good instructor. He explains clearly.
교육 기관: Kristian V
•2021년 2월 14일
awesome guided project
교육 기관: Carlos M C F
•2020년 8월 26일
Thank you
교육 기관: Igor K
•2021년 8월 15일
Awesome!
교육 기관: David W
•2020년 11월 10일
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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