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Deploying ML Web App on Google Kubernetes Engine -Autopilot
Muhammad Ali
Muhammad Ali
By the end of this project, we will be having a hands-on practical experience of creating a Google cloud project, running dog breed classification Streamlit web app locally, creating a docker image of our machine learning web app and saving it in Google...
Read more
By the end of this project, we will be having a hands-on practical experience of creating a Google cloud project, running dog breed classification Streamlit web app locally, creating a docker image of our machine learning web app and saving it in Google...
Read more
By the end of this project, we will be having a hands-on practical experience of creating a Google cloud project, running dog breed classification Streamlit web app locally, creating a docker image of our machine learning web app and saving it in Google Container Registry (GCR), creating a GKE-Autopilot cluster, creating a Kubernetes deployment and service, testing the web app running on GKE-Autopilot and finally, deleting the project to avoid incurring charges to our Google Cloud account.
Register for this course and see more details by visiting:
OpenCourser.com/course/azra2m/deploying
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for learners with experience in cloud computing and machine learning
Focuses on practical skills, including creating a Google Cloud project and using Docker to deploy a web app to GKE-Autopilot
Students should have a strong understanding of Google Cloud Platform
Requires access to a computer with Docker and Kubernetes installed
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Deploying ML Web App on Google Kubernetes Engine -Autopilot.
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Career center
Learners who complete Deploying ML Web App on Google Kubernetes Engine -Autopilot will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning (ML) Engineers are experts in the field of Machine Learning and are responsible for designing, building, and maintaining ML models. This course may be useful for individuals aspiring to become Machine Learning Engineers as it provides hands-on experience in creating and deploying ML web applications on Google Kubernetes Engine (GKE) - Autopilot.
Data Scientist
Data Scientist
Data Scientists are professionals who are skilled in collecting, analyzing, and interpreting data to gain insights and make predictions. This course may be useful for individuals aiming to become Data Scientists as it provides a practical understanding of deploying ML web applications on GKE - Autopilot, a valuable skill in data-driven organizations.
Cloud Architect
Cloud Architect
Cloud Architects design, build, and manage cloud computing systems. This course may be useful for individuals pursuing a career as Cloud Architects as it provides hands-on experience in deploying ML web applications on GKE - Autopilot, a platform that is widely used in cloud computing environments.
DevOps Engineer
DevOps Engineer
DevOps Engineers are responsible for bridging the gap between development and operations teams. This course may be useful for DevOps Engineers who work with ML applications as it provides hands-on experience in deploying ML web applications on GKE - Autopilot and managing the underlying infrastructure.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software systems. This course may be useful for Software Engineers who specialize in developing web applications as it provides practical experience in deploying ML web applications on GKE - Autopilot.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and delivering projects. This course may be useful for Project Managers who work on ML projects as it provides a practical understanding of deploying ML web applications on GKE - Autopilot and managing project deliverables.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. This course may be useful for Business Analysts who work with ML applications as it provides a practical understanding of deploying ML web applications on GKE - Autopilot and understanding the business value of ML solutions.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, analyzing, and interpreting data to identify trends and patterns. This course may be useful for Data Analysts who work with ML applications as it provides a practical understanding of deploying ML web applications on GKE - Autopilot and extracting insights from data.
Product Manager
Product Manager
Product Managers are responsible for defining, planning, and launching products. This course may be useful for Product Managers who work with ML applications as it provides a practical understanding of deploying ML web applications on GKE - Autopilot and understanding the needs of users.
IT Manager
IT Manager
IT Managers are responsible for planning, implementing, and managing IT systems. This course may be useful for IT Managers who work with ML applications as it provides a practical understanding of deploying ML web applications on GKE - Autopilot and managing IT infrastructure.
Technical Writer
Technical Writer
Technical Writers are responsible for creating and maintaining technical documentation. This course may be useful for Technical Writers who specialize in writing documentation for ML applications as it provides a practical understanding of deploying ML web applications on GKE - Autopilot.
Systems Analyst
Systems Analyst
Systems Analysts are responsible for analyzing and designing IT systems. This course may be useful for Systems Analysts who work with ML applications as it provides a practical understanding of deploying ML web applications on GKE - Autopilot and designing system architectures.
Computer Systems Analyst
Computer Systems Analyst
Computer Systems Analysts are responsible for analyzing, designing, and implementing computer systems. This course may be useful for Computer Systems Analysts who work with ML applications as it provides a practical understanding of deploying ML web applications on GKE - Autopilot and managing computer systems.
IT Specialist
IT Specialist
IT Specialists are responsible for providing technical support and maintenance for IT systems. This course may be useful for IT Specialists who work with ML applications as it provides a practical understanding of deploying ML web applications on GKE - Autopilot and troubleshooting technical issues.
Computer Network Architect
Computer Network Architect
Computer Network Architects are responsible for designing, implementing, and managing computer networks. This course may be useful for Computer Network Architects who work with ML applications as it provides a practical understanding of deploying ML web applications on GKE - Autopilot and managing network infrastructure.
For more career information including salaries, visit:
OpenCourser.com/course/azra2m/deploying
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
Deploying ML Web App on Google Kubernetes Engine -Autopilot.
Save
Provides a comprehensive overview of deep learning, a subfield of machine learning that has achieved state-of-the-art results on a wide range of tasks. It valuable resource for anyone who wants to learn more about deep learning and its applications.
Is commonly used as a textbook at academic institutions or by industry professionals. It provides a comprehensive overview of Kubernetes, an open-source container orchestration system. It covers the basics of Kubernetes, as well as more advanced topics such as deploying and managing containerized applications.
Save
Is commonly used as a textbook at academic institutions or by industry professionals. It provides a comprehensive overview of DevOps, a set of practices and principles for improving the collaboration between development and operations teams.
Is commonly used as a textbook in academic institutions or by industry professionals. It provides a practical introduction to Docker, a containerization platform. It covers the basics of Docker, as well as more advanced topics such as building and managing Docker images.
Provides a comprehensive overview of cloud computing, including its history, architecture, and various services. It valuable resource for anyone who wants to learn more about cloud computing and its applications.
Provides a comprehensive overview of site reliability engineering (SRE), a set of practices and principles for ensuring the reliability and performance of complex systems. It valuable resource for anyone who wants to learn more about SRE and its applications.
Provides a comprehensive overview of big data analytics with Hadoop, an open-source framework for distributed computing. It valuable resource for anyone who wants to learn more about big data analytics and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/azra2m/deploying
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Muhammad Ali
Good to know
Suitable for learners with experience in cloud computing and machine learning
Focuses on practical skills, including creating a Google Cloud project and using Docker to deploy a web app to GKE-Autopilot
Students should have a strong understanding of Google Cloud Platform
Requires access to a computer with Docker and Kubernetes installed
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