TensorFlow Serving with Docker for Model Deployment
Learn how to deploy your TensorFlow models with Docker using TensorFlow Serving, and get the confidence to deploy your models in production with this online course. ▼
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Course Feature
Cost:
Paid
Provider:
Coursera
Certificate:
Paid Certification
Language:
English
Start Date:
24th Jul, 2023
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [August 31st, 2023]
What does this course tell?
(Please note that the following overview content is from Alison)
This project provides a hands-on guide to deploying deep learning models using TensorFlow Serving with Docker. It is 15 hours long and teaches learners how to train and export TensorFlow models for text classification, 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 growing use of machine learning and AI, it is essential for data scientists and machine learning engineers to know how to deploy models to production. This project gives learners the skills to quickly push their TensorFlow models from development to production. Prerequisites include familiarity with Python and prior experience with building models with Keras or TensorFlow. This course is best suited for learners in North America.
We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
What skills and knowledge will you acquire during this course?
By taking this course, learners will acquire the skills and knowledge to deploy deep learning models using TensorFlow Serving with Docker. They will learn how to train and export TensorFlow models for text classification, deploy models with TF Serving and Docker in 90 seconds, and build simple gRPC and REST-based clients in Python for model inference. Additionally, learners will gain a solid real-world foundation of pushing their TensorFlow models from development to production in no time.
lHow does this course contribute to professional growth?
This course provides an invaluable opportunity for data scientists and machine learning engineers to gain the skills necessary to deploy models to production. Through hands-on guided projects, learners will gain a solid real-world foundation of pushing TensorFlow models from development to production in no time. With the prerequisite knowledge of Python and prior experience with building models with Keras or TensorFlow, learners will be able to gain the skills necessary to deploy models to production, allowing them to stay ahead of the curve in the ever-evolving world of machine learning and AI.
Is this course suitable for preparing further education?
This course is suitable for preparing further education as it provides learners with a solid real-world foundation of pushing TensorFlow models from development to production. It is designed for those who are familiar with Python and have prior experience with building models with Keras or TensorFlow. The course covers topics such as training and exporting TensorFlow models for text classification, deploying models with TF Serving and Docker, and building simple gRPC and REST-based clients in Python for model inference.
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