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Google Cloud
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
Read more
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
Read more
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
Register for this course and see more details by visiting:
OpenCourser.com/course/98qy0z/java
What's inside
Syllabus
Introduction to Advanced Machine Learning on Google Cloud
Architecting Production ML Systems
Designing Adaptable ML Systems
Designing High-Performance ML Systems
Read more
Syllabus
Introduction to Advanced Machine Learning on Google Cloud
Architecting Production ML Systems
Designing Adaptable ML Systems
Designing High-Performance ML Systems
Read more
Building Hybrid ML Systems
Summary
Course Resources
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Emphasizes practical skills with hands-on labs and interactive materials
Teaches skills that are heavily relevant to industry
Instructors are recognized experts in Machine Learning
Course is part of a series
Requires some basic knowledge in Machine Learning
Save this course
Save Java: Using Maps (Interactive) to your list so you can find it easily later:
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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 Java: Using Maps (Interactive) with these
activities:
Review java
Show steps
Strengthen your understanding of Java to prepare for the course.
Browse courses on
Java
Show steps
-
Recall basic programming concepts in Java.
-
Review object-oriented programming features in Java.
-
Practice writing Java code.
Review Linear Algebra
Show steps
Refresh your understanding of linear algebra concepts to strengthen your mathematical foundation for ML.
Browse courses on
Linear Algebra
Show steps
-
Review notes or textbooks on linear algebra
-
Practice solving linear algebra problems
Explore TensorFlow tutorials
Show steps
Familiarize yourself with TensorFlow through guided tutorials.
Show steps
-
Find official TensorFlow tutorials.
-
Work through hands-on examples.
-
Implement the concepts in your own projects.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Attend an ML-Focused Workshop or Conference
Show steps
Supplement your theoretical knowledge by attending workshops or conferences centered around ML, where you can interact with industry professionals and learn from their experiences.
Show steps
-
Explore upcoming ML-related events and workshops
-
Attend sessions led by experts, engage in discussions, and ask questions to gain insights
TensorFlow Tutorials for Beginners
Show steps
Expand your knowledge of TensorFlow abstraction levels and distributed training strategies by following a beginners guide to TensorFlow tutorials.
Browse courses on
TensorFlow
Show steps
-
Check out the TensorFlow documentation
-
Complete a beginner-friendly tutorial on distributed training with TensorFlow
Build a simple ML model
Show steps
Apply your foundational knowledge by developing a basic ML model.
Show steps
-
Define the problem statement.
-
Gather and prepare data.
-
Choose an appropriate ML algorithm.
-
Train and evaluate the model.
Practice ML Model Development with TensorFlow
Show steps
Enhance your practical ML modeling skills by completing exercises and drills focused on using TensorFlow.
Browse courses on
TensorFlow
Show steps
-
Work through a series of coding exercises using TensorFlow
-
Participate in online coding challenges or hackathons related to ML model development with TensorFlow
-
Build and train your own ML model using TensorFlow, following best practices and industry standards
Collaborate on ML Projects with Peers
Show steps
Enhance your learning by collaborating with peers on ML projects, exchanging ideas, and providing constructive feedback.
Show steps
-
Join or create a study group with classmates and work together on ML projects
-
Participate in online forums or discussion boards dedicated to ML, engaging with other learners and experts
Develop a Resource Compilation for ML Techniques
Show steps
Reinforce your understanding by creating a comprehensive compilation of resources, tools, and techniques related to ML.
Show steps
-
Conduct thorough research to identify relevant resources and tools
-
Organize and categorize the resources based on their topics, methodologies, or applications
-
Summarize and document the key findings, providing your own insights and interpretations
Implement TensorFlow Models with Custom Estimators
Show steps
Write and implement custom estimators for distributed training models in TensorFlow to enhance your practical ML skills.
Show steps
-
Create a custom estimator class
-
Implement the model definition and training loop
-
Use the custom estimator with distributed training methods
Contribute to Open Source ML Projects
Show steps
Gain practical experience and contribute to the ML community by volunteering your skills on open source projects.
Show steps
-
Identify open source ML projects that align with your interests and skills
-
Reach out to the project maintainers and express your interest in contributing
-
Work on assigned tasks or suggest your own ideas to improve the project
Develop a Custom ML Model with Advanced Features
Show steps
Apply your knowledge of advanced ML techniques by creating a custom model that incorporates unique features and demonstrates your understanding of the course concepts.
Show steps
-
Identify a real-world problem that can be addressed with a custom ML model
-
Gather and preprocess the necessary data for training the model
-
Design and implement the model architecture, leveraging advanced ML techniques
-
Train and evaluate the model, optimizing its performance through hyperparameter tuning
-
Deploy and monitor the model in a production environment, ensuring its accuracy and efficiency
Review java
Show steps
Strengthen your understanding of Java to prepare for the course.
Browse courses on
Java
Show steps
Show steps
Show steps
- Recall basic programming concepts in Java.
- Review object-oriented programming features in Java.
- Practice writing Java code.
Review Linear Algebra
Show steps
Refresh your understanding of linear algebra concepts to strengthen your mathematical foundation for ML.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review notes or textbooks on linear algebra
- Practice solving linear algebra problems
Explore TensorFlow tutorials
Show steps
Familiarize yourself with TensorFlow through guided tutorials.
Show steps
Show steps
Show steps
- Find official TensorFlow tutorials.
- Work through hands-on examples.
- Implement the concepts in your own projects.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Attend an ML-Focused Workshop or Conference
Show steps
Supplement your theoretical knowledge by attending workshops or conferences centered around ML, where you can interact with industry professionals and learn from their experiences.
Show steps
Show steps
Show steps
- Explore upcoming ML-related events and workshops
- Attend sessions led by experts, engage in discussions, and ask questions to gain insights
TensorFlow Tutorials for Beginners
Show steps
Expand your knowledge of TensorFlow abstraction levels and distributed training strategies by following a beginners guide to TensorFlow tutorials.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Check out the TensorFlow documentation
- Complete a beginner-friendly tutorial on distributed training with TensorFlow
Build a simple ML model
Show steps
Apply your foundational knowledge by developing a basic ML model.
Show steps
Show steps
Show steps
- Define the problem statement.
- Gather and prepare data.
- Choose an appropriate ML algorithm.
- Train and evaluate the model.
Practice ML Model Development with TensorFlow
Show steps
Enhance your practical ML modeling skills by completing exercises and drills focused on using TensorFlow.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Work through a series of coding exercises using TensorFlow
- Participate in online coding challenges or hackathons related to ML model development with TensorFlow
- Build and train your own ML model using TensorFlow, following best practices and industry standards
Collaborate on ML Projects with Peers
Show steps
Enhance your learning by collaborating with peers on ML projects, exchanging ideas, and providing constructive feedback.
Show steps
Show steps
Show steps
- Join or create a study group with classmates and work together on ML projects
- Participate in online forums or discussion boards dedicated to ML, engaging with other learners and experts
Develop a Resource Compilation for ML Techniques
Show steps
Reinforce your understanding by creating a comprehensive compilation of resources, tools, and techniques related to ML.
Show steps
Show steps
Show steps
- Conduct thorough research to identify relevant resources and tools
- Organize and categorize the resources based on their topics, methodologies, or applications
- Summarize and document the key findings, providing your own insights and interpretations
Implement TensorFlow Models with Custom Estimators
Show steps
Write and implement custom estimators for distributed training models in TensorFlow to enhance your practical ML skills.
Show steps
Show steps
Show steps
- Create a custom estimator class
- Implement the model definition and training loop
- Use the custom estimator with distributed training methods
Contribute to Open Source ML Projects
Show steps
Gain practical experience and contribute to the ML community by volunteering your skills on open source projects.
Show steps
Show steps
Show steps
- Identify open source ML projects that align with your interests and skills
- Reach out to the project maintainers and express your interest in contributing
- Work on assigned tasks or suggest your own ideas to improve the project
Develop a Custom ML Model with Advanced Features
Show steps
Apply your knowledge of advanced ML techniques by creating a custom model that incorporates unique features and demonstrates your understanding of the course concepts.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed with a custom ML model
- Gather and preprocess the necessary data for training the model
- Design and implement the model architecture, leveraging advanced ML techniques
- Train and evaluate the model, optimizing its performance through hyperparameter tuning
- Deploy and monitor the model in a production environment, ensuring its accuracy and efficiency
Career center
Learners who complete Java: Using Maps (Interactive) will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer will utilize their technical prowess to design, implement, automate, and monitor production ML systems, from static, dynamic, and continuous training to static and dynamic inference and batch and online processing. Machine Learning Engineers leverage TensorFlow abstraction levels and employ distributed training models to achieve business objectives via machine learning.
Data Scientist
Data Scientist
Data Scientists optimize their toolkit by learning to implement various flavors of production ML systems, and delve into TensorFlow abstraction levels and distributed training models. These professionals leverage this knowledge to design adaptable and performant ML systems and may even lead the creation of hybrid ML systems on the cloud.
Software Engineer
Software Engineer
Software Engineers may work on the implementation of ML systems and find it beneficial to gain familiarity with TensorFlow abstraction levels and distributed training of models. Individuals in this role are responsible for the design, development, and maintenance of software systems and can leverage this knowledge to contribute more effectively to ML-focused teams.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing systems. They may be tasked with designing production ML systems or contributing to the design of such systems and could benefit from this course's exploration of different flavors of production ML systems, TensorFlow abstraction levels, and writing distributed training models with custom estimators. It may also assist Cloud Architects in designing better-optimized ML systems.
Data Engineer
Data Engineer
Data Engineers may need to work with ML systems as part of their data processing and management responsibilities. This course may be useful as it can help build a foundation in the implementation of production ML systems, familiarity with TensorFlow abstraction levels, and skills for working with distributed training models.
DevOps Engineer
DevOps Engineer
DevOps Engineers may collaborate with teams implementing production ML systems or be responsible for the deployment and management of ML systems. Gaining knowledge of TensorFlow abstraction levels and distributed training of models can help DevOps Engineers be more effective when working on ML projects.
Product Manager
Product Manager
Product Managers may need to work with technical teams to define the requirements and specifications for production ML systems. This course can provide a foundation for understanding the different flavors of production ML systems and the processes involved in their implementation, helping Product Managers make more informed decisions and communicate more effectively with technical teams.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts may use ML techniques in their work and may find it beneficial to gain a deeper understanding of production ML systems and the use of TensorFlow abstraction levels. This course can help build a foundation in these areas, enhancing their ability to apply ML to financial and risk analysis.
Business Analyst
Business Analyst
Business Analysts may be involved in projects that utilize ML systems and may find it helpful to gain a better understanding of how these systems are implemented. This course can provide a foundation in the different flavors of production ML systems and the processes involved in their implementation, helping Business Analysts better understand the technical aspects of ML projects and communicate more effectively with technical teams.
Data Analyst
Data Analyst
Data Analysts may need to work with ML systems to analyze and interpret data. This course can provide a foundation in the different flavors of production ML systems and the processes involved in their implementation, helping Data Analysts better understand the technical aspects of ML systems and communicate more effectively with technical teams.
IT Manager
IT Manager
IT Managers may be responsible for overseeing the implementation and management of ML systems. This course can provide a foundation in the different flavors of production ML systems and the processes involved in their implementation, helping IT Managers make more informed decisions and provide better support to technical teams.
Project Manager
Project Manager
Project Managers may be responsible for managing projects that involve the implementation of ML systems. This course can provide a foundation in the different flavors of production ML systems and the processes involved in their implementation, helping Project Managers better understand the technical aspects of ML projects and manage them more effectively.
Systems Engineer
Systems Engineer
Systems Engineers may be involved in the design and implementation of ML systems. This course can provide a foundation in the different flavors of production ML systems and the processes involved in their implementation, helping Systems Engineers contribute more effectively to ML projects.
Network Engineer
Network Engineer
Network Engineers may be involved in the design and implementation of network infrastructure for ML systems. This course may be useful as it can help build a foundation in the different flavors of production ML systems.
Database Administrator
Database Administrator
Database Administrators may be responsible for managing databases used by ML systems. This course may be useful as it can help build a foundation in the different flavors of production ML systems and the processes involved in their implementation.
For more career information including salaries, visit:
OpenCourser.com/course/98qy0z/java
Reading list
We've selected 11 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
Java: Using Maps (Interactive).
Save
Comprehensive introduction to deep learning, covering a wide range of topics from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about deep learning.
Practical guide to using PyTorch and Scikit-Learn for machine learning. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about PyTorch and Scikit-Learn.
Practical guide to using Scikit-Learn, Keras, and TensorFlow for machine learning. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about Scikit-Learn, Keras, and TensorFlow.
Save
Practical guide to using Go for machine learning. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about machine learning.
Practical guide to using R for machine learning. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about machine learning.
Practical guide to using Java for machine learning. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about machine learning.
Practical guide to using C++ for machine learning. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about machine learning.
Practical guide to using Python for deep learning. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about deep learning.
Comprehensive introduction to pattern recognition and machine learning. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about pattern recognition and machine learning.
Comprehensive introduction to machine intelligence. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about machine intelligence.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/98qy0z/java
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Time
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Via
Pluralsight
Instructor
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Language
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Good to know
Emphasizes practical skills with hands-on labs and interactive materials
Teaches skills that are heavily relevant to industry
Instructors are recognized experts in Machine Learning
Course is part of a series
Requires some basic knowledge in Machine Learning
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