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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to deploy a Streamlit app integrated with Gemini Pro on Cloud Run.
Register for this course and see more details by visiting:
OpenCourser.com/course/1kyvnc/deploy
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What's inside
Syllabus
Traffic lights
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Read about what's good
what should give you pause and
possible dealbreakers
Focuses on deploying Streamlit apps with Gemini Pro on Cloud Run, which is directly applicable to cloud-based application development and deployment workflows
Presented by Google Cloud, which is recognized for its cloud computing services and contributions to cloud-native technologies and application deployment
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Reviews summary
Deploying streamlit and gemini pro
According to learners, this course provides a clear and straightforward guide (positive) for deploying a Streamlit app integrated with Gemini Pro on Cloud Run. Many find it a useful, hands-on lab (positive) that helps solidify understanding of the deployment process on Google Cloud. The instructions are generally easy to follow (positive), although some reviewers mention needing prior experience with Cloud Run and Streamlit (warning) to fully grasp the concepts. A few encountered minor issues with the lab environment or specific steps (warning), but overall, it is seen as a practical and valuable exercise (positive) for developers looking to deploy AI-powered web apps.
Beneficial with prior Cloud Run or Streamlit knowledge.
"Good lab, but it helps if you have some prior familiarity with Streamlit and Cloud Run."
"While the instructions are clear, having a basic understanding of the technologies involved makes it easier."
"I think learners new to Cloud Run might need to supplement this lab with other resources."
"Requires a foundational knowledge of Google Cloud services."
Offers practical, hands-on practice with deployment.
"Provides practical hands-on experience in deploying a real-world application."
"I found the hands-on nature of the lab incredibly helpful."
"The lab allows you to get your hands dirty and practice the deployment steps directly."
"It's a good practical exercise for deploying web applications on Cloud Run."
Provides a clear, step-by-step deployment guide.
"This lab provided very clear steps on how to deploy the Streamlit App to Cloud Run, incorporating Gemini Pro."
"The steps were very clear and easy to follow, walking you through the entire process."
"A straightforward and easy-to-follow lab for deploying Streamlit apps on Cloud Run."
"I appreciated the clarity of the deployment instructions provided in the lab."
Some users faced small issues or errors.
"Encountered a small issue that required backtracking, but resolved it following the guide carefully."
"There were a couple of steps that seemed slightly off, but I managed to work through them."
"I hit a minor error during deployment, likely due to a typo on my part, but the lab was helpful for debugging."
"Had a brief moment of confusion on one step, but overall smooth."
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 Deploy a Streamlit App Integrated with Gemini Pro on Cloud Run with these
activities:
Review Python Fundamentals
Show steps
Reviewing Python fundamentals will ensure a smoother experience when working with Streamlit and Gemini Pro.
Browse courses on
Python Basics
Show steps
-
Complete a Python tutorial covering basic syntax and data structures.
-
Practice writing simple Python scripts.
-
Review object-oriented programming concepts in Python.
Brush up on Docker Skills
Show steps
Familiarizing yourself with Docker will help you understand how Streamlit apps are containerized for deployment.
Browse courses on
Docker Containers
Show steps
-
Learn how to create Dockerfiles.
-
Practice building and running Docker images.
-
Understand Docker Compose for multi-container applications.
Follow Streamlit Tutorials
Show steps
Working through Streamlit tutorials will provide hands-on experience with building and deploying Streamlit applications.
Show steps
-
Complete the official Streamlit tutorial.
-
Explore advanced Streamlit features like caching and session state.
-
Find and follow tutorials on deploying Streamlit apps to various platforms.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Read 'Effective Streamlit for Data Science'
Show steps
Reading this book will provide a deeper understanding of Streamlit and its capabilities.
View
Putting Knowledge to Work
on Amazon
Show steps
-
Read the book cover to cover.
-
Experiment with the code examples in the book.
-
Apply the concepts learned in the book to your own Streamlit projects.
Document Your Streamlit App
Show steps
Creating documentation for your Streamlit app will reinforce your understanding of the code and deployment process.
Show steps
-
Write a README file explaining the app's purpose and functionality.
-
Document the code using docstrings.
-
Create a user guide for the app.
Expand the Streamlit App
Show steps
Adding new features to the Streamlit app will solidify your understanding of Streamlit, Gemini Pro, and Cloud Run.
Show steps
-
Add new functionality to the Streamlit app using Gemini Pro.
-
Implement user authentication.
-
Optimize the app for performance.
Contribute to a Streamlit Project
Show steps
Contributing to an open-source Streamlit project will provide valuable experience working with a real-world codebase.
Show steps
-
Find an open-source Streamlit project on GitHub.
-
Identify a bug or feature to work on.
-
Submit a pull request with your changes.
Review Python Fundamentals
Show steps
Reviewing Python fundamentals will ensure a smoother experience when working with Streamlit and Gemini Pro.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Complete a Python tutorial covering basic syntax and data structures.
- Practice writing simple Python scripts.
- Review object-oriented programming concepts in Python.
Brush up on Docker Skills
Show steps
Familiarizing yourself with Docker will help you understand how Streamlit apps are containerized for deployment.
Browse courses on
Docker Containers
Show steps
Show steps
Show steps
- Learn how to create Dockerfiles.
- Practice building and running Docker images.
- Understand Docker Compose for multi-container applications.
Follow Streamlit Tutorials
Show steps
Working through Streamlit tutorials will provide hands-on experience with building and deploying Streamlit applications.
Show steps
Show steps
Show steps
- Complete the official Streamlit tutorial.
- Explore advanced Streamlit features like caching and session state.
- Find and follow tutorials on deploying Streamlit apps to various platforms.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Read 'Effective Streamlit for Data Science'
Show steps
Reading this book will provide a deeper understanding of Streamlit and its capabilities.
View
Putting Knowledge to Work
on Amazon
Show steps
Show steps
Show steps
- Read the book cover to cover.
- Experiment with the code examples in the book.
- Apply the concepts learned in the book to your own Streamlit projects.
Document Your Streamlit App
Show steps
Creating documentation for your Streamlit app will reinforce your understanding of the code and deployment process.
Show steps
Show steps
Show steps
- Write a README file explaining the app's purpose and functionality.
- Document the code using docstrings.
- Create a user guide for the app.
Expand the Streamlit App
Show steps
Adding new features to the Streamlit app will solidify your understanding of Streamlit, Gemini Pro, and Cloud Run.
Show steps
Show steps
Show steps
- Add new functionality to the Streamlit app using Gemini Pro.
- Implement user authentication.
- Optimize the app for performance.
Contribute to a Streamlit Project
Show steps
Contributing to an open-source Streamlit project will provide valuable experience working with a real-world codebase.
Show steps
Show steps
Show steps
- Find an open-source Streamlit project on GitHub.
- Identify a bug or feature to work on.
- Submit a pull request with your changes.
Career center
Learners who complete Deploy a Streamlit App Integrated with Gemini Pro on Cloud Run will develop knowledge and skills
that may be useful to these careers:
Cloud Application Developer
Cloud Application Developer
A Cloud Application Developer builds and maintains applications that run on cloud platforms, leveraging services like Cloud Run. This course is directly relevant as it guides learners through deploying a Streamlit application integrated with Gemini Pro on Cloud Run, a practical skill for any application developer in the cloud. The hands-on experience of deploying an application helps those entering the field to better understand the practicalities of cloud based development. The specific use of Streamlit and Gemini Pro within the course, moreover, provides insight into how modern machine learning and web application frameworks operate within cloud services.
AI Application Developer
AI Application Developer
An AI Application Developer builds applications that leverage artificial intelligence and machine learning models. This course provides the learner with experience in using the Gemini Pro model within the context of a web application on the cloud. The hands-on experience of deploying an app with both web and machine learning components is very valuable in the field of AI application development. An AI Application Developer needs to understand how to bring AI models to users, which this course demonstrates. Moreover, using Streamlit as a web framework alongside an LLM gives real world context for developing AI solutions.
Cloud Engineer
Cloud Engineer
A Cloud Engineer focuses on implementing and maintaining cloud infrastructure and services. This course provides the user with direct experience in deploying a Streamlit application integrated with Gemini Pro on Cloud Run. This hands-on experience is very important to a Cloud Engineer, as it provides insight into cloud deployment processes. Cloud Engineers need to understand how to deploy applications on different cloud services. The practical nature of the lab also allows the Cloud Engineer to learn from a technical perspective.
DevOps Engineer
DevOps Engineer
A DevOps Engineer focuses on automating and streamlining the software development and deployment process. This course helps by showing how to use Cloud Run to deploy an application. Such a hands-on experience with cloud deployment provides a great foundation for those seeking careers in DevOps. This course covers the key principles of continuous integration and continuous deployment (CI/CD) by guiding the user through the steps of taking software from a development environment (Streamlit) to production (Cloud Run). Learning how to deploy applications to a cloud platform is crucial to the work of a DevOps engineer.
Web Application Developer
Web Application Developer
A Web Application Developer specializes in building interactive web applications. This course provides valuable experience by walking the student through building a Streamlit web interface connected to a large language model, and deploying the application on Cloud Run. A web application developer must understand how to go from a local development environment to a production environment. This course, therefore, provides great benefits to aspiring Web Application Developers. Moreover, understanding how to integrate machine learning models into web applications is highly valuable.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course provides practical experience in how to deploy a web application that has an integrated machine learning model, specifically on Google's Cloud Run. The experience of building and deploying such a system is very helpful to a Software Engineer. While this course may not cover all the aspects of software engineering, it provides crucial experience in integrating machine learning components and cloud services. The lab format of the course ensures the Software Engineer can learn through hands-on practice.
Backend Developer
Backend Developer
A Backend Developer focuses on the server-side logic and databases of applications, often including cloud deployments. This course provides practical experience of deploying a Streamlit application integrated with Gemini Pro on Cloud Run. This is valuable because a Backend Developer must know how to take software into production. The course provides direct experience with a cloud based deployment, which is very useful for a Backend Developer. Moreover, understanding how to use services like Cloud Run is essential in modern backend development.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer focuses on building, deploying, and maintaining machine learning models and systems. This course may be helpful because it demonstrates how to integrate the Gemini Pro model with a Streamlit application, then deploy it on Google's Cloud Run. This is an example of an end-to-end pipeline useful for a Machine Learning Engineer. Moreover, this course provides an understanding of how to utilize large language models (LLMs) within a web app, which is a major topic in the current machine learning arena. The lab's focus on practical deployment on the cloud ensures a Machine Learning Engineer knows how to take models into production.
Cloud Solutions Architect
Cloud Solutions Architect
A Cloud Solutions Architect designs and implements cloud-based solutions to meet business and technical requirements, using services like Cloud Run. This course provides direct insights into how a particular application, a Streamlit app integrated with Gemini Pro, can be deployed on a cloud platform. This knowledge is very important, as a Cloud Solutions Architect must understand how different application types function within a cloud environment. The practical nature of the lab gives the architect a deeper understanding of the deployment process, which helps with designing more robust and efficient cloud solutions. Also, the course's focus on a specific stack provides a tangible example of how cloud resources can be used effectively.
Technical Consultant
Technical Consultant
A Technical Consultant advises clients on technical solutions and strategies. Understanding how to deploy applications on cloud platforms is crucial for a technical consultant. This course provides the learner with a practical example of how to take an application from a development environment to a cloud deployment. A Technical Consultant should understand how different cloud services work. By taking this course, they will see a specific example that they can use to discuss with clients. Moreover, the course’s integration of machine learning tools can also be helpful.
Technical Trainer
Technical Trainer
A Technical Trainer designs and delivers training programs on technical topics. This course may be useful for a Technical Trainer because it provides a practical example of how to deploy an application on the Google Cloud Platform. Understanding specific technologies through completing hands-on labs, such as the one provided in this course, can help a trainer in curriculum development or in answering student questions. The trainer should ensure that their students are keeping up with the times, and this course is a good example of how to use modern technologies.
Data Scientist
Data Scientist
A Data Scientist analyzes data to generate insights, often using machine learning models. This course may be useful because it offers experience in integrating an LLM (Gemini Pro) with a UI (Streamlit) and then deploying it. This demonstrates the Data Scientist's role in bringing a machine learning model into the hands of a user. Moreover, the course provides skills in using Streamlit, which is a popular tool for building data science applications. While a Data Scientist spends most time on modeling the data, seeing how their work can become a product is very useful.
Data Engineer
Data Engineer
A Data Engineer focuses on building and maintaining the data infrastructure for an organization, including cloud systems. While this course focuses primarily on the application side, it provides some insight into how a cloud application is deployed and functions. This course may be useful because it shows how to deploy an application that makes use of a machine learning model. The practical, hands-on nature of the course will additionally help any Data Engineer who wants to see all the parts of a pipeline including user interface and deployment.
Research Scientist
Research Scientist
A Research Scientist engages in scientific research and experimentation. This course may be useful because it demonstrates the integration and deployment of an LLM using cloud based services. While this course is focused on the practical rather than theoretical side of research, scientists today must often take their research prototypes into production. The hands-on nature of the lab should be helpful in understanding how to use existing services within the research context.
Startup Founder
Startup Founder
A Startup Founder is responsible for the overall strategy and operation of a new company. This course may be useful for a Startup Founder who wishes to understand more about how cloud services can be used in practice. The hands-on approach of this course provides a practical look at how machine learning and web applications are typically deployed. While a Founder may not do the technical work themselves, understanding how the work happens is crucial to the success of the company. The founder, thereby, will better see how technology can realize their vision.
For more career information including salaries, visit:
OpenCourser.com/course/1kyvnc/deploy
Reading list
We've selected one 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
Deploy a Streamlit App Integrated with Gemini Pro on Cloud Run.
Provides a comprehensive guide to building and deploying Streamlit applications for data science. It covers a wide range of topics, from basic Streamlit concepts to advanced techniques for optimizing performance and scalability. This book is particularly useful for understanding how to integrate Streamlit with other data science tools and libraries. It provides additional depth to the course by exploring real-world use cases and best practices.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1kyvnc/deploy
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