Deploying a Pytorch Computer Vision Model API to Heroku from Coursera

Welcome to the “Deploying a Pytorch Computer Vision Model API to Heroku” guided project.

In this project, we will deploy a Flask REST API using one of Pytorch's pre-trained computer vision image classification models. This API will be able to receive an image, inference the pre-trained model, and return its predicted classification.

This project is an intermediate python project for anyone interested in learning about how to productionize Pytorch computer vision models in the real world via a REST API on Heroku. It requires preliminary knowledge on how to build and train PyTorch models (as we will not be building or training models), how to utilize Git and a fundamental understanding of REST APIs. Learners would also need a Heroku account and some familiarity with the Python Flask module and the Postman API Platform.

At the end of this project, learners will have a publicly available API they can use to demonstrate their knowledge in deploying computer vision models.

What's inside

Syllabus

Project Overview

Computer vision is one of the prominent fields of AI with numerous applications in the real world including self-driving cars, image recognition, and object tracking, among others. The ability to make models available for real-world use is an essential skill anyone interested in AI engineering should have especially for computer vision and this is why this project exists. In this project, we will deploy a Flask REST API using one of Pytorch's pre-trained computer vision image classification models. This API will be able to receive an image, inference the pre-trained model, and return its predicted classification. This project is an intermediate python project for anyone interested in learning about how to productionize Pytorch computer vision models in the real world via a REST API on Heroku. It requires preliminary knowledge on how to build and train PyTorch models (as we will not be building or training models), how to utilize Git and a fundamental understanding of REST APIs. Learners would also need a Heroku account and some familiarity with the Python Flask module and the Postman API Platform. At the end of this project, learners will have a publicly available API they can use to demonstrate their knowledge in deploying computer vision models.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides valuable skills for those seeking a career as an AI engineer with a focus on computer vision and deep learning

Suitable for individuals with some experience in Python, PyTorch, and computer vision

Builds upon existing knowledge of computer vision, deep learning, and REST APIs

Requires familiarity with Python, PyTorch, Heroku, and the Flask module

Provides practical experience in deploying computer vision models to the real world

Involves hands-on practice and interactive materials

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 Deploying a Pytorch Computer Vision Model API to Heroku with these activities:

Review Pytorch Basics

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Refresh your understanding of Pytorch basics, including tensors, neural networks, and optimization techniques, to ensure a solid foundation for the course.

Browse courses on PyTorch

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Review Pytorch documentation or tutorials.
Work through Pytorch exercises or examples.
Create a small Pytorch project.

Advanced Computer Vision: Techniques and Applications

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Review advanced computer vision techniques and their applications to expand your theoretical understanding and prepare you for more complex projects.

View Computer Vision on Amazon

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Read selected chapters and sections of the book.
Take notes and summarize key concepts.
Discuss the material with classmates or a study group.

Classify images using Pytorch Models

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Practice classifying images using pre-trained Pytorch models to reinforce your understanding of the model's capabilities and limitations.

Browse courses on Image Classification

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Load and preprocess an image dataset.
Select a pre-trained Pytorch model for image classification.
Classify the images using the selected model.
Analyze the classification results.

Four other activities

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Show all seven activities

Deploy a Computer Vision Model on Heroku

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Follow a guided tutorial to deploy a computer vision model on Heroku, gaining practical experience in setting up a REST API and integrating it with your model.

Browse courses on Model Deployment

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Create a Heroku account and install the Heroku CLI.
Set up a Flask application with a REST API.
Deploy the application to Heroku.
Test the API by sending requests and analyzing responses.

Write a Blog Post on Computer Vision Trends

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Write a blog post summarizing the latest trends in computer vision, providing insights into emerging technologies and their potential impact on the field.

Browse courses on Computer Vision

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Research and gather information on current computer vision trends.
Outline the structure of your blog post.
Write and edit the content.
Publish and promote your blog post.

Build a Custom Computer Vision Project

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Apply your knowledge by building a custom computer vision project, demonstrating your ability to train, evaluate, and deploy a model for a specific task.

Browse courses on Project Development

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Define the project scope and requirements.
Gather and prepare a dataset.
Train a custom computer vision model.
Evaluate the model's performance.
Deploy the model and create a user interface.

Contribute to an Open Source Computer Vision Project

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Immerse yourself in the computer vision community by contributing to an open source project, gaining practical experience in collaborative development and issue resolution.

Browse courses on Open Source

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Identify an open source computer vision project.
Review the project's codebase and documentation.
Find and work on an issue or feature request.
Submit and iterate on your contributions.

Career center

Learners who complete Deploying a Pytorch Computer Vision Model API to Heroku will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

Computer Vision Engineers design, develop, and implement computer vision systems for various industries such as manufacturing, healthcare, and retail. They work on tasks such as image processing, object detection, and pattern recognition. This course can help build a foundation in computer vision principles and techniques, which is essential for success in this role. By learning about pre-trained computer vision models and how to deploy them, learners can gain hands-on experience that is directly applicable to real-world scenarios.

See salaries and explore the career path for Computer Vision Engineer

Machine Learning Engineer

Machine Learning Engineers develop and maintain machine learning systems for a variety of applications. They work on tasks such as data preprocessing, model training, and deployment. This course provides a solid foundation in machine learning concepts and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can expand their skill set and become more well-rounded machine learning engineers.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use data analysis techniques to extract insights from data and solve business problems. They work on tasks such as data exploration, model building, and visualization. This course provides a strong foundation in data analysis principles and techniques, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly valuable in the data science field.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, and maintain software systems. They work on tasks such as coding, testing, and debugging. This course provides a foundation in software engineering principles and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly important in the software engineering field.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers are responsible for the planning, development, and launch of new products. They work with engineers, designers, and marketers to bring products to market that meet customer needs. This course provides a foundation in product management principles and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly important in the product management field.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts analyze business needs and develop solutions to improve efficiency and profitability. They work with stakeholders to gather requirements, identify pain points, and develop recommendations. This course provides a foundation in business analysis principles and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly important in the business analysis field.

See salaries and explore the career path for Business Analyst

Marketing Manager

Marketing Managers are responsible for the planning, execution, and measurement of marketing campaigns. They work with a variety of teams to develop and implement marketing strategies that reach target audiences and achieve business goals. This course provides a foundation in marketing principles and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly important in the marketing field.

See salaries and explore the career path for Marketing Manager

Sales Manager

Sales Managers are responsible for leading and motivating sales teams to achieve sales goals. They work with customers to identify needs, develop proposals, and close deals. This course provides a foundation in sales principles and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly important in the sales field.

See salaries and explore the career path for Sales Manager

Customer Success Manager

Customer Success Managers are responsible for ensuring that customers are satisfied with their products or services. They work with customers to resolve issues, provide support, and identify opportunities for growth. This course provides a foundation in customer success principles and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly important in the customer success field.

See salaries and explore the career path for Customer Success Manager

Account Manager

Account Managers are responsible for managing relationships with existing customers. They work with customers to ensure that they are satisfied with their products or services, and they identify opportunities for upselling and cross-selling. This course provides a foundation in account management principles and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly important in the account management field.

See salaries and explore the career path for Account Manager

Technical Writer

Technical Writers create documentation for a variety of products and services. They work with engineers, designers, and other stakeholders to develop user manuals, white papers, and other materials that explain how to use and maintain products. This course provides a foundation in technical writing principles and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly important in the technical writing field.

See salaries and explore the career path for Technical Writer

Project Manager

Project Managers are responsible for planning, executing, and closing projects. They work with stakeholders to define project goals, develop project plans, and track progress. This course provides a foundation in project management principles and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly important in the project management field.

See salaries and explore the career path for Project Manager

Data Analyst

Data Analysts collect, analyze, and interpret data to identify trends and patterns. They work with stakeholders to develop insights that can be used to improve decision-making. This course provides a foundation in data analysis principles and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly important in the data analysis field.

See salaries and explore the career path for Data Analyst

UX Designer

UX Designers design user interfaces for websites and mobile applications. They work with engineers and other stakeholders to create user experiences that are easy to use and enjoyable. This course provides a foundation in UX design principles and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly important in the UX design field.

See salaries and explore the career path for UX Designer

Quality Assurance Analyst

Quality Assurance Analysts test software products to ensure that they meet requirements and are free of defects. They work with engineers and other stakeholders to identify and resolve issues. This course provides a foundation in quality assurance principles and practices, which is essential for success in this role. By learning about computer vision models and how to deploy them, learners can gain additional skills that are becoming increasingly important in the quality assurance field.

See salaries and explore the career path for Quality Assurance Analyst