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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...
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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.

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.
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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

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

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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
Refreshing your linear algebra and calculus skills will give you a stronger foundation for the course.
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  • Review your notes from previous courses
  • Solve practice problems
Practice coding challenges
Solve coding challenges to improve your problem-solving skills and coding proficiency.
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  • 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
Joining a study group can reinforce your learning and improve your understanding.
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  • Find a study group
  • Attend study group meetings
Seven other activities
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Show all ten activities
Practice with online coding challenges
Practice writing code and testing your understanding of concepts through online coding challenges.
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  • Find an online coding challenge platform
  • Solve coding challenges regularly
Attend a workshop on machine learning model deployment
Attending a workshop can provide you with hands-on experience and insights from experts.
Browse courses on Machine Learning
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  • Find a workshop on machine learning model deployment
  • Attend the workshop
Follow online tutorials on model deployment and optimization
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
Create a CI/CD pipeline for your model to automate the deployment process and improve code quality and reliability.
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  • 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
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
Apply the course concepts to a real-world project to deepen your understanding and gain practical experience.
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  • 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
Building a machine learning model will give you practical experience with the concepts you've learned.
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  • 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.

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 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 comprehensive guide to data analysis with Python, covering topics such as data wrangling, data visualization, and machine learning.

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