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Java
Using Maps (Interactive)
Google Cloud
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
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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
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Java: Using Maps (Interactive).
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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
16 hours
Level
Intermediate
Via
Pluralsight
Instructor
Google Cloud
Language
English
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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