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Avis et commentaires pour d'étudiants pour TensorFlow Serving with Docker for Model Deployment par Coursera Project Network

46 évaluations
9 avis

À propos du cours

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 - 9 sur 9 Avis pour TensorFlow Serving with Docker for Model Deployment

par Enzo D G M

18 oct. 2020

Introducción a tensorflow serving poderosa, muy bien explicada y con pocas líneas de código

par Gabriel I P L

26 août 2020


par Bryan R

23 avr. 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!

par Ro H

20 févr. 2021

A fantastic introduction to TF Serving.

par serdar b

18 janv. 2021

Good instructor. He explains clearly.

par Kristian V

14 févr. 2021

awesome guided project

par Carlos M C F

26 août 2020

Thank you

par Igor K

15 août 2021


par David W

10 nov. 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.