We may earn an affiliate commission when you visit our partners.
Take your machine learning skills to the next level. Learn how to develop, monitor, and deploy a model with FastAPI. Enroll in the Udacity training course today.
Prerequisite details
Read more
Take your machine learning skills to the next level. Learn how to develop, monitor, and deploy a model with FastAPI. Enroll in the Udacity training course today.
Prerequisite details
Read more
Take your machine learning skills to the next level. Learn how to develop, monitor, and deploy a model with FastAPI. Enroll in the Udacity training course today.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Basic machine learning
- Intermediate Python
- REST APIs
- Unit testing
- Basic github
- Command line interface basics
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/hfw8oz/deploying
Two deals to help you save
We found two deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Use code at checkout.
Valid for a limited time only
Bootcamp level quality
Complete hands-on projects and get personalized feedback with Udacity.
Take
40%
off
UDACITY40
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
What's inside
Syllabus
In the project, you'll develop, monitor, and deploy a classification model on publicly available Census Bureau data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners in developing, monitoring, and deploying machine learning models
Teaches how to develop, monitor, and deploy machine learning models, which are skills in high demand in industry
Uses publicly available Census Bureau data, making it relevant to real-world applications
Provides a hands-on project that allows learners to apply their knowledge
Requires learners to come in with basic machine learning, intermediate Python, and other prerequisite knowledge
Advised to take other courses first as prerequisites, which may not be readily available or accessible
Save this course
Save Deploying a Machine Learning Model with FastAPI to your list so you can find it easily later:
Save
Save
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 Machine Learning Model with FastAPI with these
activities:
Review basic machine learning concepts
Show steps
A basic understanding of machine learning is required for this course. Reviewing these concepts will strengthen the foundation for the course content.
Browse courses on
Machine Learning
Show steps
-
Read introductory articles or tutorials
-
Take practice quizzes or complete exercises
Explore Udacity's `FastAPI` course materials
Show steps
This course heavily utilizes `FastAPI`, and reviewing extra materials on `FastAPI` will aid in understanding the concepts presented in the course.
Browse courses on
FastAPI
Show steps
-
Navigate to the Udacity course page
-
Review the course syllabus and modules
-
Follow along with the video tutorials
Solve coding challenges on LeetCode
Show steps
The course requires proficiency in Python. Solving coding challenges helps reinforce Python skills and improves problem-solving abilities.
Browse courses on
Python
Show steps
-
Create a LeetCode account
-
Choose a problem to solve
-
Implement a solution in Python
-
Submit the solution for evaluation
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group or discussion forum
Show steps
Engaging with peers helps reinforce concepts, provides diverse perspectives, and fosters a supportive learning environment.
Show steps
-
Identify or create a study group or discussion forum
-
Participate in regular discussions
-
Share knowledge and insights
Build a simple REST API using `FastAPI`
Show steps
Hands-on practice with `FastAPI` is essential for understanding its concepts and functionalities. Building a simple API allows students to apply their knowledge.
Browse courses on
FastAPI
Show steps
-
Design the API endpoints and data model
-
Implement the API endpoints using `FastAPI`
-
Configure routing and request handling
-
Test the API using a client or postman
Gather resources on best practices for developing and deploying machine learning models
Show steps
This course focuses on developing and deploying machine learning models. Gathering resources will provide additional insights and support for this process.
Browse courses on
Machine Learning
Show steps
-
Search for articles, tutorials, and documentation
-
Organize the resources into a central location
-
Review and synthesize the information
Participate in a machine learning hackathon
Show steps
Hackathons provide a practical and collaborative environment to apply machine learning skills. Participating in a hackathon challenges students and enhances their understanding.
Browse courses on
Machine Learning
Show steps
-
Find a relevant hackathon
-
Form a team or work individually
-
Develop and present a machine learning solution
Review basic machine learning concepts
Show steps
A basic understanding of machine learning is required for this course. Reviewing these concepts will strengthen the foundation for the course content.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Read introductory articles or tutorials
- Take practice quizzes or complete exercises
Explore Udacity's `FastAPI` course materials
Show steps
This course heavily utilizes `FastAPI`, and reviewing extra materials on `FastAPI` will aid in understanding the concepts presented in the course.
Browse courses on
FastAPI
Show steps
Show steps
Show steps
- Navigate to the Udacity course page
- Review the course syllabus and modules
- Follow along with the video tutorials
Solve coding challenges on LeetCode
Show steps
The course requires proficiency in Python. Solving coding challenges helps reinforce Python skills and improves problem-solving abilities.
Browse courses on
Python
Show steps
Show steps
Show steps
- Create a LeetCode account
- Choose a problem to solve
- Implement a solution in Python
- Submit the solution for evaluation
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group or discussion forum
Show steps
Engaging with peers helps reinforce concepts, provides diverse perspectives, and fosters a supportive learning environment.
Show steps
Show steps
Show steps
- Identify or create a study group or discussion forum
- Participate in regular discussions
- Share knowledge and insights
Build a simple REST API using `FastAPI`
Show steps
Hands-on practice with `FastAPI` is essential for understanding its concepts and functionalities. Building a simple API allows students to apply their knowledge.
Browse courses on
FastAPI
Show steps
Show steps
Show steps
- Design the API endpoints and data model
- Implement the API endpoints using `FastAPI`
- Configure routing and request handling
- Test the API using a client or postman
Gather resources on best practices for developing and deploying machine learning models
Show steps
This course focuses on developing and deploying machine learning models. Gathering resources will provide additional insights and support for this process.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Search for articles, tutorials, and documentation
- Organize the resources into a central location
- Review and synthesize the information
Participate in a machine learning hackathon
Show steps
Hackathons provide a practical and collaborative environment to apply machine learning skills. Participating in a hackathon challenges students and enhances their understanding.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a relevant hackathon
- Form a team or work individually
- Develop and present a machine learning solution
Career center
Learners who complete Deploying a Machine Learning Model with FastAPI will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will design, develop, and maintain machine learning systems. You will use your knowledge of machine learning algorithms, data analysis, and software engineering to build models that can solve complex problems. This course will help you build a foundation in machine learning and provide you with the skills you need to develop and deploy your own models. The focus on REST APIs and unit testing will also prepare you for the challenges of working in a team environment.
Data Scientist
Data Scientist
Data Scientists use machine learning and other statistical techniques to extract insights from data. They work with a variety of data sources, from structured data to unstructured text. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in data analysis. The focus on REST APIs and unit testing will also be helpful if you plan to work in a team environment.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with a variety of programming languages and technologies, and they must be able to understand and implement complex algorithms. This course will help you build a foundation in machine learning and provide you with the skills you need to develop and deploy machine learning models. The focus on REST APIs and unit testing will also prepare you for the challenges of working in a team environment.
Data Analyst
Data Analyst
Data Analysts gather, clean, and analyze data to help businesses make better decisions. They use a variety of statistical and machine learning techniques to identify trends and patterns in data. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in data analysis. The focus on REST APIs and unit testing will also be helpful if you plan to work in a team environment.
Business Analyst
Business Analyst
Business Analysts help businesses understand their data and make better decisions. They work with a variety of stakeholders, including business leaders, IT professionals, and data scientists. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in data analysis. The focus on REST APIs and unit testing will also be helpful if you plan to work in a team environment.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with a variety of stakeholders, including engineers, designers, and marketers. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in data analysis. The focus on REST APIs and unit testing will also be helpful if you plan to work in a team environment.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They work with a variety of financial institutions, including investment banks, hedge funds, and insurance companies. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in data analysis. The focus on REST APIs and unit testing will also be helpful if you plan to work in a team environment.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex problems in a variety of industries. They work with a variety of stakeholders, including engineers, business analysts, and data scientists. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in data analysis. The focus on REST APIs and unit testing will also be helpful if you plan to work in a team environment.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They work with a variety of data sources, from structured data to unstructured text. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in machine learning. The focus on REST APIs and unit testing will also be helpful if you plan to work in a team environment.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. They work with a variety of programming languages and technologies, and they must be able to understand and implement complex algorithms. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in artificial intelligence. The focus on REST APIs and unit testing will also prepare you for the challenges of working in a team environment.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work with a variety of data sources, from structured data to unstructured text. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in data engineering. The focus on REST APIs and unit testing will also be helpful if you plan to work in a team environment.
Database Administrator
Database Administrator
Database Administrators design, build, and maintain databases. They work with a variety of database technologies, and they must be able to understand and implement complex queries. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in database administration. The focus on REST APIs and unit testing will also be helpful if you plan to work in a team environment.
Software Tester
Software Tester
Software Testers test software applications to ensure that they meet requirements. They work with a variety of testing tools and techniques, and they must be able to identify and report bugs. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in software testing. The focus on REST APIs and unit testing will also be helpful if you plan to work in a team environment.
Technical Writer
Technical Writer
Technical Writers create documentation for software applications and other technical products. They work with a variety of stakeholders, including engineers, designers, and marketers. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in technical writing. The focus on REST APIs and unit testing will also be helpful if you plan to work in a team environment.
Customer Success Manager
Customer Success Manager
Customer Success Managers work with customers to ensure that they are successful with a product or service. They help customers with onboarding, training, and troubleshooting. This course will provide you with the skills you need to develop and deploy machine learning models, and it will also help you build a strong foundation in customer success management. The focus on REST APIs and unit testing will also be helpful if you plan to work in a team environment.
For more career information including salaries, visit:
OpenCourser.com/course/hfw8oz/deploying
Reading list
We've selected 13 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 a Machine Learning Model with FastAPI.
Comprehensive guide to deep learning using Python. It covers the latest techniques and algorithms in deep learning, and it includes many hands-on exercises.
Practical guide to machine learning using Scikit-Learn, Keras, and TensorFlow. It provides a comprehensive overview of machine learning algorithms and techniques, and it includes many hands-on exercises.
Comprehensive guide to natural language processing using Python. It covers the basics of natural language processing, as well as more advanced topics such as deep learning for natural language processing.
Comprehensive introduction to speech and language processing. It covers the basics of speech and language processing, as well as more advanced topics such as deep learning for speech and language processing.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It great resource for anyone who wants to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of Bayesian data analysis. It great resource for anyone who wants to learn more about Bayesian statistics.
Provides a comprehensive overview of statistical learning. It great resource for anyone who wants to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning. It great resource for anyone who wants to learn more about the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of data mining concepts and techniques. It great resource for anyone who wants to learn more about how to mine data for knowledge.
Provides a comprehensive overview of machine learning in action. It great resource for anyone who wants to learn more about how to use machine learning to solve real-world problems.
Provides a comprehensive overview of deep learning for coders using Fastai and PyTorch. It great resource for anyone who wants to learn more about how to use deep learning to solve real-world problems.
Comprehensive introduction to reinforcement learning. It covers the basics of reinforcement learning, as well as more advanced topics such as deep reinforcement learning.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It great resource for anyone who wants to learn more about computer vision.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hfw8oz/deploying
Share
Similar courses
Here are nine courses similar to
Deploying a Machine Learning Model with FastAPI.
Deploying Machine Learning Models in Production
OpenCourser.com/course/ckn42i/deploying
Deploying Machine Learning Models in Production
Implementing and Operating AWS Machine Learning Solutions
OpenCourser.com/course/2796or/implementing
Implementing and Operating AWS Machine Learning Solutions
Microsoft Azure AI Engineer: Developing ML Pipelines in Microsoft Azure
OpenCourser.com/course/huywy4/microsoft
Microsoft Azure AI Engineer: Developing ML Pipelines in...
MLOps in R: Deploying machine learning models using vetiver
OpenCourser.com/course/th0cji/mlops
MLOps in R: Deploying machine learning models using...
Monitor and Evaluate Model Performance During Training
OpenCourser.com/course/d1qml9/monitor
Monitor and Evaluate Model Performance During Training
Machine Learning Using SAS Viya
OpenCourser.com/course/8qd9zd/machine
Machine Learning Using SAS Viya
Deployment of Machine Learning Models
OpenCourser.com/course/c0ggy8/deployment
Deployment of Machine Learning Models
MuleSoft 4 Fundamentals
OpenCourser.com/course/1jkp4d/mulesoft
MuleSoft 4 Fundamentals
MLOps for Scaling TinyML
OpenCourser.com/course/y4mu4y/mlops
MLOps for Scaling TinyML
Effort
4 weeks
Via
Udacity
Language
English
Good to know
Builds a strong foundation for beginners in developing, monitoring, and deploying machine learning models
Teaches how to develop, monitor, and deploy machine learning models, which are skills in high demand in industry
Uses publicly available Census Bureau data, making it relevant to real-world applications
Provides a hands-on project that allows learners to apply their knowledge
Requires learners to come in with basic machine learning, intermediate Python, and other prerequisite knowledge
Advised to take other courses first as prerequisites, which may not be readily available or accessible
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser