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Snehan Kekre
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...
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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...
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/c6x1zm/tensorflow
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what to watch for , and
possible dealbreakers
Teaches how to deploy deep learning models into production, which is a valuable skill for data scientists and machine learning engineers
Uses TensorFlow Serving with Docker, which is an industry-standard tool for deploying deep learning models
Offers hands-on experience, which is crucial for learners to develop practical skills
Tailored for learners who need to push their TensorFlow models from development to production quickly, which caters to their specific needs
Requires familiarity with Python and Keras or TensorFlow, which may limit accessibility for complete beginners
Currently only available to learners in the North America region, which limits accessibility for learners in other regions
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Save TensorFlow Serving with Docker for Model Deployment to your list so you can find it easily later:
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Reviews summary
Real-world tensorflow model deployment
This hands-on, 1.5-hour course teaches you how to deploy deep learning models to production using TensorFlow Serving and Docker. The course includes building simple gRPC and REST-based clients in Python for model inference. With the increasing adoption of machine learning and AI, this course gives learners a solid, real-world foundation to go from TensorFlow model development to deployment quickly.
Easy to follow for beginners
"A fantastic introduction to TF Serving."
Excellent instructor
"Good instructor. He explains clearly."
Course is practical
"This guided project gives learners a solid, real-world foundation of pushing your TensorFlow models from development to production in no time!"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in TensorFlow Serving with Docker for Model Deployment with these
activities:
Connect with ML engineers
Show steps
Seek guidance and mentorship from experienced ML engineers to accelerate your learning.
Show steps
-
Attend industry events
-
Reach out to professionals on LinkedIn
Review Docker for deep learning
Show steps
Refresh your Docker skills to ensure you have a solid foundation for deploying models with Docker.
Browse courses on
Docker
Show steps
-
Review Docker documentation
-
Complete Docker tutorials
Practice text classification with TensorFlow
Show steps
Enhance your understanding of text classification by practicing with TensorFlow.
Browse courses on
Text Classification
Show steps
-
Work through TensorFlow tutorials
-
Complete text classification exercises
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a workshop on deep learning deployment
Show steps
Gain hands-on experience and practical insights by attending a workshop focused on deep learning deployment.
Show steps
-
Identify relevant workshops
-
Register and attend the workshop
Set up gRPC for model inference
Show steps
Gain proficiency in setting up gRPC for effective model inference.
Browse courses on
gRPC
Show steps
-
Follow gRPC tutorials
-
Build a gRPC client for model inference
Create REST API for model inference
Show steps
Develop your skills in creating REST APIs for seamless model inference.
Browse courses on
REST API
Show steps
-
Learn REST API fundamentals
-
Build a REST API for model inference
Deploy a deep learning model to production
Show steps
Put your knowledge into practice by deploying a deep learning model to production.
Browse courses on
Model Deployment
Show steps
-
Choose a deployment platform
-
Prepare model for deployment
-
Deploy model to production
-
Monitor and evaluate model performance
Connect with ML engineers
Show steps
Seek guidance and mentorship from experienced ML engineers to accelerate your learning.
Show steps
Show steps
Show steps
- Attend industry events
- Reach out to professionals on LinkedIn
Review Docker for deep learning
Show steps
Refresh your Docker skills to ensure you have a solid foundation for deploying models with Docker.
Browse courses on
Docker
Show steps
Show steps
Show steps
- Review Docker documentation
- Complete Docker tutorials
Practice text classification with TensorFlow
Show steps
Enhance your understanding of text classification by practicing with TensorFlow.
Browse courses on
Text Classification
Show steps
Show steps
Show steps
- Work through TensorFlow tutorials
- Complete text classification exercises
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a workshop on deep learning deployment
Show steps
Gain hands-on experience and practical insights by attending a workshop focused on deep learning deployment.
Show steps
Show steps
Show steps
- Identify relevant workshops
- Register and attend the workshop
Set up gRPC for model inference
Show steps
Gain proficiency in setting up gRPC for effective model inference.
Browse courses on
gRPC
Show steps
Show steps
Show steps
- Follow gRPC tutorials
- Build a gRPC client for model inference
Create REST API for model inference
Show steps
Develop your skills in creating REST APIs for seamless model inference.
Browse courses on
REST API
Show steps
Show steps
Show steps
- Learn REST API fundamentals
- Build a REST API for model inference
Deploy a deep learning model to production
Show steps
Put your knowledge into practice by deploying a deep learning model to production.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Choose a deployment platform
- Prepare model for deployment
- Deploy model to production
- Monitor and evaluate model performance
Career center
Learners who complete TensorFlow Serving with Docker for Model Deployment will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers create models that enhance the capabilities of software. They use TensorFlow to build and deploy models that can make predictions or take actions based on data. TensorFlow Serving and Docker simplify the deployment of models, making them easier for Machine Learning Engineers to put their work into production.
Software Engineer
Software Engineer
Software Engineers build software applications. With the rise of machine learning, Software Engineers are increasingly using TensorFlow to add ML capabilities to their applications. TensorFlow Serving and Docker make it easier for Software Engineers to deploy ML models within their applications.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data. They often use TensorFlow to build models that can identify patterns and trends in data. TensorFlow Serving and Docker make it easier for Data Scientists to deploy their models so that other teams can use them.
DevOps Engineer
DevOps Engineer
DevOps Engineers are responsible for ensuring that software is built, tested, and deployed reliably. With the rise of machine learning, DevOps Engineers are increasingly using TensorFlow Serving and Docker to deploy ML models. This course teaches DevOps Engineers how to use these tools to simplify the deployment process and ensure that ML models are deployed reliably.
Business Analyst
Business Analyst
Business Analysts work with stakeholders to understand their needs and develop solutions that meet those needs. With the rise of machine learning, Business Analysts are increasingly using TensorFlow to build models that can help businesses make better decisions. TensorFlow Serving and Docker make it easier for Business Analysts to deploy their models to production.
Product Manager
Product Manager
Product Managers are responsible for defining, planning, and launching new products. With the rise of machine learning, Product Managers are increasingly using TensorFlow to build models that can help them understand customer needs and improve product quality. TensorFlow Serving and Docker make it easier for Product Managers to deploy their models to production.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns that generate leads and sales. With the rise of machine learning, Marketing Managers are increasingly using TensorFlow to build models that can help them target their campaigns more effectively. TensorFlow Serving and Docker make it easier for Marketing Managers to deploy their models to production.
Sales Engineer
Sales Engineer
Sales Engineers work with customers to understand their needs and recommend solutions that meet those needs. With the rise of machine learning, Sales Engineers are increasingly using TensorFlow to build models that can help them identify potential customers and close deals. TensorFlow Serving and Docker make it easier for Sales Engineers to deploy their models to production.
Technical Writer
Technical Writer
Technical Writers create documentation that explains how to use software and products. With the rise of machine learning, Technical Writers are increasingly using TensorFlow to build models that can help them generate documentation automatically. TensorFlow Serving and Docker make it easier for Technical Writers to deploy their models to production.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. With the rise of machine learning, Quantitative Analysts are increasingly using TensorFlow to build models that can help them make better investment decisions. TensorFlow Serving and Docker make it easier for Quantitative Analysts to deploy their models to production.
Risk Manager
Risk Manager
Risk Managers identify and assess risks that can impact an organization. With the rise of machine learning, Risk Managers are increasingly using TensorFlow to build models that can help them identify and mitigate risks. TensorFlow Serving and Docker make it easier for Risk Managers to deploy their models to production.
Fraud Analyst
Fraud Analyst
Fraud Analysts investigate fraudulent activities and develop strategies to prevent fraud. With the rise of machine learning, Fraud Analysts are increasingly using TensorFlow to build models that can help them identify and prevent fraud. TensorFlow Serving and Docker make it easier for Fraud Analysts to deploy their models to production.
Security Analyst
Security Analyst
Security Analysts protect an organization's data and systems from cyber threats. With the rise of machine learning, Security Analysts are increasingly using TensorFlow to build models that can help them identify and prevent cyber threats. TensorFlow Serving and Docker make it easier for Security Analysts to deploy their models to production.
For more career information including salaries, visit:
OpenCourser.com/course/c6x1zm/tensorflow
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
TensorFlow Serving with Docker for Model Deployment.
Provides a comprehensive introduction to machine learning with TensorFlow. It covers topics such as data preprocessing, model training, and evaluation.
Provides a practical introduction to deep learning with Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a conceptual introduction to deep learning. It covers topics such as neural networks, backpropagation, and optimization.
Provides a hands-on introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers topics such as data preprocessing, model training, and evaluation.
Provides a comprehensive guide to deep learning with TensorFlow. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a practical guide to using Docker. It covers topics such as containerization, image building, and orchestration.
Provides a comprehensive introduction to machine learning with R. It covers topics such as data preprocessing, model training, and evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c6x1zm/tensorflow
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Good to know
Teaches how to deploy deep learning models into production, which is a valuable skill for data scientists and machine learning engineers
Uses TensorFlow Serving with Docker, which is an industry-standard tool for deploying deep learning models
Offers hands-on experience, which is crucial for learners to develop practical skills
Tailored for learners who need to push their TensorFlow models from development to production quickly, which caters to their specific needs
Requires familiarity with Python and Keras or TensorFlow, which may limit accessibility for complete beginners
Currently only available to learners in the North America region, which limits accessibility for learners in other regions
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