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TensorFlow Serving with Docker for Model Deployment に戻る

Coursera Project Network による TensorFlow Serving with Docker for Model Deployment の受講者のレビューおよびフィードバック



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....



TensorFlow Serving with Docker for Model Deployment: 1 - 9 / 9 レビュー

by Enzo D G M


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

by Gabriel I P L



by Bryan R


Very well structured. It took a little longer that the 1.5 hours but the time was well spent. Nice job by the instructor!

by Ro H


A fantastic introduction to TF Serving.

by serdar b


Good instructor. He explains clearly.

by Kristian V


awesome guided project

by Carlos M C F


Thank you

by Igor K



by David W


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.