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Laurence Moroney
Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
Read more
Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
Read more
Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
In this final course, you’ll explore four different scenarios you’ll encounter when deploying models. You’ll be introduced to TensorFlow Serving, a technology that lets you do inference over the web. You’ll move on to TensorFlow Hub, a repository of models that you can use for transfer learning. Then you’ll use TensorBoard to evaluate and understand how your models work, as well as share your model metadata with others. Finally, you’ll explore federated learning and how you can retrain deployed models with user data while maintaining data privacy.
This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
Register for this course and see more details by visiting:
OpenCourser.com/course/8wfr3e/advanced
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What's inside
Syllabus
TensorFlow Extended
Sharing pre-trained models with TensorFlow Hub
Tensorboard: tools for model training
Read more
Syllabus
TensorFlow Extended
Sharing pre-trained models with TensorFlow Hub
Tensorboard: tools for model training
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Taught by Laurence Moroney, an experienced professional in this field
Covers advanced TensorFlow topics, which are relevant to industry
Delves into real-world deployment scenarios for machine learning models
Emphasizes data efficiency, a crucial skill in modern machine learning
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Reviews summary
Tensorflow deployment: advanced real-world ml applications
This course, 'Advanced Deployment Scenarios with TensorFlow,' is designed to bridge the gap between machine learning model development and real-world application. It delves into critical aspects of ML model deployment, moving beyond theoretical foundations to practical implementation. The curriculum is structured to provide learners with insights into various deployment scenarios, covering key technologies like TensorFlow Serving for web inference, TensorFlow Hub for leveraging pre-trained models, and TensorBoard for model evaluation and sharing. A unique focus is also placed on Federated Learning, addressing data privacy in retraining deployed models. Positioned as a follow-up to 'TensorFlow in Practice' and 'Deep Learning Specialization,' it aims to equip professionals with advanced skills for production-ready ML solutions.
Requires prior TensorFlow and Deep Learning foundation.
"I came into this course having completed 'TensorFlow in Practice', which was essential for understanding the content."
"A strong foundational understanding of Deep Learning concepts is definitely needed before taking this course."
"This course is not for beginners; prior TensorFlow experience and core ML knowledge are critical."
Addresses cutting-edge topic of data privacy in ML.
"I appreciated the section on Federated Learning and its approach to data privacy."
"This course introduced me to retraining deployed models while maintaining user privacy."
"The coverage of Federated Learning feels very timely and important for modern AI ethics."
Covers specialized tools like Serving and Hub.
"Learning TensorFlow Serving was a highlight for me, enabling efficient web inference."
"The insights into TensorFlow Hub are useful for leveraging pre-trained models effectively."
"TensorBoard for model evaluation provides valuable debugging and visualization capabilities."
Emphasizes real-world application of ML models.
"I found the focus on bringing models into the real world to be very practical."
"The course helped me navigate various deployment scenarios for machine learning."
"It addresses the crucial step of actual ML model deployment, which is often overlooked in other courses."
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 Advanced Deployment Scenarios with TensorFlow with these
activities:
Compile a resource collection on model deployment
Show steps
Strengthen your understanding and access to resources related to deploying machine learning models.
Browse courses on
Model Deployment
Show steps
-
Gather articles, tutorials, and documentation on model deployment
-
Organize and curate the resources for easy reference
Practice deploying models with TensorFlow Serving
Show steps
Develop hands-on skills in deploying models using TensorFlow Serving, enhancing your understanding of model deployment.
Browse courses on
TensorFlow Serving
Show steps
-
Set up a TensorFlow Serving environment
-
Deploy a trained model to the environment
-
Test the deployed model using inference requests
Evaluate models using TensorBoard
Show steps
Strengthen your understanding of model performance by utilizing TensorBoard's visualization tools.
Browse courses on
TensorBoard
Show steps
-
Install and configure TensorBoard
-
Log training and evaluation data to TensorBoard
-
Analyze visualizations to identify areas for model improvement
Two other activities
Expand to see all activities and additional details
Show all five activities
Explore TensorFlow Hub for pre-trained models
Show steps
Gain familiarity with TensorFlow Hub and its extensive collection of pre-trained models, facilitating the application of transfer learning.
Browse courses on
TensorFlow Hub
Show steps
-
Browse the TensorFlow Hub repository
-
Select and load a pre-trained model for a specific task
-
Integrate the pre-trained model into your project
Investigate Federated Learning for secure retraining
Show steps
Gain insights into the principles of Federated Learning, empowering you to update your deployed models with user data while maintaining privacy.
Browse courses on
Federated Learning
Show steps
-
Understand the concept of Federated Learning
-
Implement a Federated Learning algorithm
-
Evaluate the effectiveness and security of Federated Learning
Compile a resource collection on model deployment
Show steps
Strengthen your understanding and access to resources related to deploying machine learning models.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Gather articles, tutorials, and documentation on model deployment
- Organize and curate the resources for easy reference
Practice deploying models with TensorFlow Serving
Show steps
Develop hands-on skills in deploying models using TensorFlow Serving, enhancing your understanding of model deployment.
Browse courses on
TensorFlow Serving
Show steps
Show steps
Show steps
- Set up a TensorFlow Serving environment
- Deploy a trained model to the environment
- Test the deployed model using inference requests
Evaluate models using TensorBoard
Show steps
Strengthen your understanding of model performance by utilizing TensorBoard's visualization tools.
Browse courses on
TensorBoard
Show steps
Show steps
Show steps
- Install and configure TensorBoard
- Log training and evaluation data to TensorBoard
- Analyze visualizations to identify areas for model improvement
Two other activities
Expand to see all activities and additional details
Show all five activities
Explore TensorFlow Hub for pre-trained models
Show steps
Gain familiarity with TensorFlow Hub and its extensive collection of pre-trained models, facilitating the application of transfer learning.
Browse courses on
TensorFlow Hub
Show steps
Show steps
Show steps
- Browse the TensorFlow Hub repository
- Select and load a pre-trained model for a specific task
- Integrate the pre-trained model into your project
Investigate Federated Learning for secure retraining
Show steps
Gain insights into the principles of Federated Learning, empowering you to update your deployed models with user data while maintaining privacy.
Browse courses on
Federated Learning
Show steps
Show steps
Show steps
- Understand the concept of Federated Learning
- Implement a Federated Learning algorithm
- Evaluate the effectiveness and security of Federated Learning
Career center
Learners who complete Advanced Deployment Scenarios with TensorFlow will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers work on the design, development, testing, and maintenance of machine learning models. Machine Learning Engineers build, deploy, and evaluate machine learning models, which are used to make predictions and automate tasks. They also work on the development of new machine learning algorithms and techniques. Machine learning engineers must have an understanding of machine learning principles, as well as the ability to code and deploy machine learning models. The Advanced Deployment Scenarios with TensorFlow course will provide you with the skills and knowledge you need to build, deploy, and evaluate machine learning models, making you a more competitive and effective Machine Learning Engineer. The course will cover topics such as TensorFlow Serving, TensorFlow Hub, TensorBoard, and Federated Learning.
Data Scientist
Data Scientist
Data Scientists use data to build predictive models and gain insights into business problems. They work on a variety of tasks, such as data cleaning, feature engineering, model building, and model evaluation. Data Scientists must have an understanding of statistics, machine learning, and programming. The Advanced Deployment Scenarios with TensorFlow course will provide you with the skills and knowledge you need to build, deploy, and evaluate machine learning models, making you a more competitive and effective Data Scientist. The course will cover topics such as TensorFlow Serving, TensorFlow Hub, TensorBoard, and Federated Learning.
Software Engineer
Software Engineer
Software Engineers design, develop, test, and maintain software applications. They work on a variety of projects, such as developing web applications, mobile applications, and desktop applications. Software Engineers must have an understanding of computer science principles, as well as the ability to code and debug software. The Advanced Deployment Scenarios with TensorFlow course will provide you with the skills and knowledge you need to develop and deploy machine learning models, making you a more competitive and effective Software Engineer. The course will cover topics such as TensorFlow Serving, TensorFlow Hub, TensorBoard, and Federated Learning.
Business Analyst
Business Analyst
Business Analysts work on the analysis and interpretation of business data. They use data to identify trends, patterns, and opportunities. Business Analysts must have an understanding of business principles, as well as the ability to analyze and interpret data. The Advanced Deployment Scenarios with TensorFlow course will provide you with the skills and knowledge you need to analyze and interpret data, making you a more competitive and effective Business Analyst. The course will cover topics such as TensorFlow Serving, TensorFlow Hub, TensorBoard, and Federated Learning.
Data Architect
Data Architect
Data Architects design and build data architectures. They work on the development of data warehouses, data lakes, and other data storage systems. Data Architects must have an understanding of data management principles, as well as the ability to design and build data architectures.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They work on the installation, configuration, and maintenance of database systems.
Product Manager
Product Manager
Product Managers oversee the development and launch of products. They work on the definition of product requirements, the development of product roadmaps, and the launch of new products. Product Managers must have an understanding of business principles, as well as the ability to define product requirements and manage product development.
Project Manager
Project Manager
Project Managers oversee the planning, execution, and completion of projects. They work on the definition of project scope, the development of project plans, and the management of project teams.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation. They work on the development of user manuals, white papers, and other technical documents.
Technical Support Specialist
Technical Support Specialist
Technical Support Specialists provide support to users of software and hardware products. They work on the troubleshooting of problems, the installation of software and hardware, and the training of users.
Quality Assurance Tester
Quality Assurance Tester
Quality Assurance Testers test software and hardware products to ensure that they meet quality standards. They work on the development of test plans, the execution of tests, and the reporting of defects. Quality Assurance Testers must have an understanding of software and hardware testing principles, as well as the ability to develop and execute test plans. The Advanced Deployment Scenarios with TensorFlow course may be useful for those who wish to enter the field of Quality Assurance Testing, as it will provide you with the skills and knowledge you need to develop and execute test plans.
Sales Engineer
Sales Engineer
Sales Engineers work on the sale of software and hardware products. They work on the identification of customer needs, the development of sales proposals, and the negotiation of contracts. Sales Engineers must have an understanding of sales principles, as well as the ability to identify customer needs and develop sales proposals. The Advanced Deployment Scenarios with TensorFlow course may be useful for those who wish to enter the field of Sales Engineering, as it will provide you with the skills and knowledge you need to identify customer needs and develop sales proposals.
Marketing Specialist
Marketing Specialist
Marketing Specialists develop and implement marketing campaigns. They work on the development of marketing plans, the creation of marketing materials, and the execution of marketing campaigns. Marketing Specialists must have an understanding of marketing principles, as well as the ability to develop and implement marketing campaigns.
Operations Manager
Operations Manager
Operations Managers oversee the operation of an organization. They work on the development of operational plans, the management of operations teams, and the execution of operational activities. Operations Managers must have an understanding of business principles, as well as the ability to develop and implement operational plans.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data and make recommendations for investment decisions. They work on the development of financial models, the analysis of financial data, and the recommendation of investment decisions. Financial Analysts must have an understanding of finance principles, as well as the ability to analyze financial data and make investment recommendations.
For more career information including salaries, visit:
OpenCourser.com/course/8wfr3e/advanced
Reading list
We've selected ten 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
Advanced Deployment Scenarios with TensorFlow.
Save
Introduces the foundations of deep learning and provides a comprehensive overview of its techniques.
Provides a comprehensive overview of TensorFlow, covering its architecture, APIs, and best practices.
Provides a comprehensive overview of artificial intelligence and its techniques.
Save
Provides a comprehensive overview of machine learning techniques and their applications.
Offers hands-on projects using TensorFlow to build machine learning applications.
Provides a comprehensive overview of statistical learning and its techniques.
Provides a comprehensive overview of statistical learning and its applications using R.
Provides a comprehensive overview of machine learning techniques and their implementation in Python.
Introduces deep learning concepts and best practices using Fastai and PyTorch.
Provides hands-on experience with machine learning using Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8wfr3e/advanced
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Effort
4 weeks of study, 4-5 hours/week
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructor
Laurence Moroney
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
TensorFlow: Data and Deployment. It's comprised of four courses:
- Device-based Models with TensorFlow Lite
- Advanced Deployment Scenarios with TensorFlow (viewing)
- Data Pipelines with TensorFlow Data Services
- Browser-based Models with TensorFlow.js
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