We may earn an affiliate commission when you visit our partners.
Giacomo Vianello,
Ulrika Jägare,
Justin Clifford Smith, PhD,
Bradford Tuckfield,
and
Joshua Bernhard
This course teaches students how to robustly deploy a machine learning model into production. En route to that goal students will learn how to put the finishing touches on a model by taking a fine grained approach to model performance, checking bias, and ultimately writing a model card. Students will also learn how to version control their data and models using Data Version Control (DVC). The last piece in preparation for deployment will be learning Continuous Integration and Continuous Deployment which will be accomplished using GitHub Actions and Heroku, respectively. Finally, students will learn how to write a fast, type-checked...
Read more
This course teaches students how to robustly deploy a machine learning model into production. En route to that goal students will learn how to put the finishing touches on a model by taking a fine grained approach to model performance, checking bias, and ultimately writing a model card. Students will also learn how to version control their data and models using Data Version Control (DVC). The last piece in preparation for deployment will be learning Continuous Integration and Continuous Deployment which will be accomplished using GitHub Actions and Heroku, respectively. Finally, students will learn how to write a fast, type-checked...
Read more
This course teaches students how to robustly deploy a machine learning model into production. En route to that goal students will learn how to put the finishing touches on a model by taking a fine grained approach to model performance, checking bias, and ultimately writing a model card. Students will also learn how to version control their data and models using Data Version Control (DVC). The last piece in preparation for deployment will be learning Continuous Integration and Continuous Deployment which will be accomplished using GitHub Actions and Heroku, respectively. Finally, students will learn how to write a fast, type-checked, and auto-documented API using FastAPI.
Register for this course and see more details by visiting:
OpenCourser.com/course/v4q8tl/deploying
Here's a deal for you
We found an offer that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
UDACITY40
What's inside
Syllabus
We'll introduce you to the course concepts of operationalizing our model, focusing on the ecosystem surrounding that model to successfully deploy it, and easily maintain it in production.
Read more
Syllabus
We'll introduce you to the course concepts of operationalizing our model, focusing on the ecosystem surrounding that model to successfully deploy it, and easily maintain it in production.
Read more
In this lesson, we will cover performance testing and preparing a model for production.
In this lesson, we will review git and then delve into Data Version Control (DVC) and the concepts of data provenance.
We cover the software engineering principles of automation, testing, and versioning. We put these into action using Continuous Integration and Continuous Delivery with Heroku and Github Actions.
Delve into FastAPI which leverages type hints to build a robust and self-documenting REST API. First, build out our API locally, test it, and the deploy to Heroku where you'll test it again live.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops professional skills in operationalizing machine learning models, which is core for data scientists and engineers
Taught by instructors who are recognized experts in their fields
Provides comprehensive coverage of the machine learning deployment lifecycle, from model evaluation to deployment and monitoring
Emphasizes industry best practices and tools, such as DVC, GitHub Actions, and Heroku
Utilises FastAPI to teach the creation of robust and documented APIs
Requires students to take other courses as prerequisites
Save this course
Save Deploying a Scalable ML Pipeline in Production 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 Scalable ML Pipeline in Production with these
activities:
Review linear algebra and calculus concepts
Show steps
Refreshing your linear algebra and calculus skills will give you a stronger foundation for the course.
Browse courses on
Linear Algebra
Show steps
-
Review your notes from previous courses
-
Solve practice problems
Practice coding challenges
Show steps
Solve coding challenges to improve your problem-solving skills and coding proficiency.
Browse courses on
Coding
Show steps
-
Find coding challenges online or in books
-
Solve the challenges using your preferred programming language
-
Review your solutions and learn from your mistakes
Join a study group to discuss the course material with peers
Show steps
Joining a study group can reinforce your learning and improve your understanding.
Show steps
-
Find a study group
-
Attend study group meetings
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Practice with online coding challenges
Show steps
Practice writing code and testing your understanding of concepts through online coding challenges.
Show steps
-
Find an online coding challenge platform
-
Solve coding challenges regularly
Attend a workshop on machine learning model deployment
Show steps
Attending a workshop can provide you with hands-on experience and insights from experts.
Browse courses on
Machine Learning
Show steps
-
Find a workshop on machine learning model deployment
-
Attend the workshop
Follow online tutorials on model deployment and optimization
Show steps
Following online tutorials can help you gain additional knowledge and skills related to the course material.
Browse courses on
Model Deployment
Show steps
-
Find online tutorials on model deployment and optimization
-
Follow the tutorials and complete the exercises
Create a model pipeline
Show steps
Create a CI/CD pipeline for your model to automate the deployment process and improve code quality and reliability.
Browse courses on
CI/CD
Show steps
-
Design the pipeline architecture
-
Implement the pipeline using GitHub Actions and Heroku
-
Test and debug the pipeline
-
Deploy the pipeline
Write a blog post summarizing the key concepts of the course
Show steps
Writing a blog post will help you synthesize and solidify your understanding of the material.
Show steps
-
Identify the main concepts you want to cover
-
Write a draft of your blog post
-
Get feedback on your draft
-
Publish your blog post
Build a personal project using the course concepts
Show steps
Apply the course concepts to a real-world project to deepen your understanding and gain practical experience.
Show steps
-
Identify a project idea
-
Design and plan your project
-
Implement your project
-
Evaluate and improve your project
Build a machine learning model using the concepts learned in the course
Show steps
Building a machine learning model will give you practical experience with the concepts you've learned.
Browse courses on
Machine Learning
Show steps
-
Choose a dataset
-
Prepare your data
-
Build and train your model
-
Evaluate your model
-
Deploy your model
Review linear algebra and calculus concepts
Show steps
Refreshing your linear algebra and calculus skills will give you a stronger foundation for the course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review your notes from previous courses
- Solve practice problems
Practice coding challenges
Show steps
Solve coding challenges to improve your problem-solving skills and coding proficiency.
Browse courses on
Coding
Show steps
Show steps
Show steps
- Find coding challenges online or in books
- Solve the challenges using your preferred programming language
- Review your solutions and learn from your mistakes
Join a study group to discuss the course material with peers
Show steps
Joining a study group can reinforce your learning and improve your understanding.
Show steps
Show steps
Show steps
- Find a study group
- Attend study group meetings
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Practice with online coding challenges
Show steps
Practice writing code and testing your understanding of concepts through online coding challenges.
Show steps
Show steps
Show steps
- Find an online coding challenge platform
- Solve coding challenges regularly
Attend a workshop on machine learning model deployment
Show steps
Attending a workshop can provide you with hands-on experience and insights from experts.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a workshop on machine learning model deployment
- Attend the workshop
Follow online tutorials on model deployment and optimization
Show steps
Following online tutorials can help you gain additional knowledge and skills related to the course material.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Find online tutorials on model deployment and optimization
- Follow the tutorials and complete the exercises
Create a model pipeline
Show steps
Create a CI/CD pipeline for your model to automate the deployment process and improve code quality and reliability.
Browse courses on
CI/CD
Show steps
Show steps
Show steps
- Design the pipeline architecture
- Implement the pipeline using GitHub Actions and Heroku
- Test and debug the pipeline
- Deploy the pipeline
Write a blog post summarizing the key concepts of the course
Show steps
Writing a blog post will help you synthesize and solidify your understanding of the material.
Show steps
Show steps
Show steps
- Identify the main concepts you want to cover
- Write a draft of your blog post
- Get feedback on your draft
- Publish your blog post
Build a personal project using the course concepts
Show steps
Apply the course concepts to a real-world project to deepen your understanding and gain practical experience.
Show steps
Show steps
Show steps
- Identify a project idea
- Design and plan your project
- Implement your project
- Evaluate and improve your project
Build a machine learning model using the concepts learned in the course
Show steps
Building a machine learning model will give you practical experience with the concepts you've learned.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a dataset
- Prepare your data
- Build and train your model
- Evaluate your model
- Deploy your model
Career center
Learners who complete Deploying a Scalable ML Pipeline in Production will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. They work with data in a variety of formats, from structured data in databases to unstructured data in text documents and images. This course can help you develop the skills needed to become a Data Scientist by teaching you how to deploy a machine learning model into production. You will also learn how to use data version control to keep track of changes to your data and models.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. They work with a variety of programming languages and technologies to create software that meets the needs of users. This course can help you develop the skills needed to become a Software Engineer by teaching you how to deploy a machine learning model into production. You will also learn how to use continuous integration and continuous delivery to automate the software development process.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for the entire lifecycle of a machine learning model, from conceptualizing and designing new models to testing and deploying them into production. This course can help you develop the skills needed to become a Machine Learning Engineer by teaching you how to deploy a machine learning model into production. You will also learn how to check for bias in your models and write a model card, which is a document that describes the model's purpose, limitations, and potential risks.
Data Analyst
Data Analyst
A Data Analyst collects, cleans, and analyzes 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 can help you develop the skills needed to become a Data Analyst by teaching you how to deploy a machine learning model into production. You will also learn how to write a fast, type-checked, and auto-documented API using FastAPI.
Risk Analyst
Risk Analyst
A Risk Analyst identifies and assesses risks to businesses. They develop strategies to mitigate those risks and protect the business from financial loss. This course can help you develop the skills needed to become a Risk Analyst by teaching you how to deploy a machine learning model into production. You will also learn how to use continuous integration and continuous delivery to automate the software development process.
Business Analyst
Business Analyst
A Business Analyst helps businesses understand their needs and develop solutions to meet those needs. They work with stakeholders from all levels of the organization to gather requirements, analyze data, and recommend solutions. This course can help you develop the skills needed to become a Business Analyst by teaching you how to deploy a machine learning model into production. You will also learn how to write a model card and communicate the results of your analysis to stakeholders.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. They develop trading strategies and make investment recommendations based on their analysis. This course can help you develop the skills needed to become a Quantitative Analyst by teaching you how to deploy a machine learning model into production. You will also learn how to use data version control to keep track of changes to your data and models.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products and features. They work with engineers, designers, and marketers to ensure that products meet the needs of users. This course can help you develop the skills needed to become a Product Manager by teaching you how to deploy a machine learning model into production. You will also learn how to gather feedback from users and use that feedback to improve your products.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical techniques to solve business problems. They develop models to optimize operations and improve efficiency. This course may help you develop the skills needed to become an Operations Research Analyst by teaching you how to deploy a machine learning model into production. You will also learn how to use data version control to keep track of changes to your data and models.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations. They work with clients to develop investment portfolios and manage risk. This course can help you develop the skills needed to become a Financial Analyst by teaching you how to deploy a machine learning model into production. You will also learn how to write a fast, type-checked, and auto-documented API using FastAPI.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. They develop statistical models to make predictions and draw conclusions. This course may help you develop the skills needed to become a Statistician by teaching you how to deploy a machine learning model into production. You will also learn how to use continuous integration and continuous delivery to automate the software development process.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. They ensure that databases are running smoothly and that data is secure. This course may help you develop the skills needed to become a Database Administrator by teaching you how to deploy a machine learning model into production. You will also learn how to use data version control to keep track of changes to your data and models.
IT Project Manager
IT Project Manager
An IT Project Manager plans and manages IT projects. They work with stakeholders to define project requirements, develop project plans, and track project progress. This course may help you develop the skills needed to become an IT Project Manager by teaching you how to deploy a machine learning model into production. You will also learn how to use continuous integration and continuous delivery to automate the software development process.
Data Engineer
Data Engineer
A Data Engineer builds and maintains the infrastructure that supports data analysis. They work with data scientists and other stakeholders to ensure that data is available and accessible for analysis. This course may help you develop the skills needed to become a Data Engineer by teaching you how to deploy a machine learning model into production. You will also learn how to write a fast, type-checked, and auto-documented API using FastAPI.
Software Tester
Software Tester
A Software Tester tests software to identify bugs and defects. They work with developers to fix bugs and improve the quality of software. This course may help you develop the skills needed to become a Software Tester by teaching you how to deploy a machine learning model into production. You will also learn how to write a fast, type-checked, and auto-documented API using FastAPI.
For more career information including salaries, visit:
OpenCourser.com/course/v4q8tl/deploying
Reading list
We've selected nine 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 Scalable ML Pipeline in Production.
A concise guide to deploying a machine learning model into production. It covers topics such as model training, evaluation, deployment, and monitoring.
A practical guide to machine learning, covering a wide range of topics from data preprocessing to model deployment.
A comprehensive guide to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks.
A comprehensive guide to data science, covering topics such as data exploration, data visualization, and machine learning.
A classic textbook on statistical learning, covering topics such as linear regression, logistic regression, and support vector machines.
A gentle introduction to machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning.
A beginner-friendly guide to machine learning, covering topics such as data preprocessing, model training, and model evaluation.
A comprehensive guide to machine learning in R, covering topics such as data preprocessing, model training, and model evaluation.
A comprehensive guide to data analysis with Python, covering topics such as data wrangling, data visualization, and machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/v4q8tl/deploying
Share
Similar courses
Here are nine courses similar to
Deploying a Scalable ML Pipeline in Production.
MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning
OpenCourser.com/course/jb74ed/mlops2
MLOps2 (Azure): Data Pipeline Automation & Optimization...
Most relevant
MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services
OpenCourser.com/course/pb1k05/mlops2
MLOps2 (AWS): Data Pipeline Automation & Optimization...
Most relevant
MLOps2 (GCP): Data Pipeline Automation & Optimization using Google Cloud Platform
OpenCourser.com/course/u9i3wp/mlops2
MLOps2 (GCP): Data Pipeline Automation & Optimization...
Most relevant
Deployment of Machine Learning Models
OpenCourser.com/course/c0ggy8/deployment
Deployment of Machine Learning Models
Most relevant
ICAgile (ICP-FDO): Continuous Integration
OpenCourser.com/course/siw91s/icagile
ICAgile (ICP-FDO): Continuous Integration
Most relevant
Deploying Value with Kanban
OpenCourser.com/course/s9e5ea/deploying
Deploying Value with Kanban
Operationalizing Microsoft Azure AI Solutions
OpenCourser.com/course/ehcsts/operationalizing
Operationalizing Microsoft Azure AI Solutions
Hands-on with AWS: Software Development Practices
OpenCourser.com/course/xk7osv/hands
Hands-on with AWS: Software Development Practices
Automate R scripts with GitHub Actions: Deploy a model
OpenCourser.com/course/b2b217/automate
Automate R scripts with GitHub Actions: Deploy a model
Time
40320 hours
Level
Advanced
Via
Udacity
Instructors
Giacomo Vianello
Ulrika Jägare
Justin Clifford Smith, PhD
Bradford Tuckfield
Joshua Bernhard
Language
English
Good to know
Develops professional skills in operationalizing machine learning models, which is core for data scientists and engineers
Taught by instructors who are recognized experts in their fields
Provides comprehensive coverage of the machine learning deployment lifecycle, from model evaluation to deployment and monitoring
Emphasizes industry best practices and tools, such as DVC, GitHub Actions, and Heroku
Utilises FastAPI to teach the creation of robust and documented APIs
Requires students to take other courses as prerequisites
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