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Deploying a Scalable ML Pipeline in Production

Giacomo Vianello, Ulrika Jägare, Justin Clifford Smith, PhD, Bradford Tuckfield, and Joshua Bernhard

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Bootcamp level quality

Complete hands-on projects and get personalized feedback with Udacity.

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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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

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Refreshing your linear algebra and calculus skills will give you a stronger foundation for the course.

Browse courses on Linear Algebra

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Review your notes from previous courses
Solve practice problems

Practice coding challenges

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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

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Joining a study group can reinforce your learning and improve your understanding.

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Find a study group
Attend study group meetings

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Practice with online coding challenges

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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

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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

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Following online tutorials can help you gain additional knowledge and skills related to the course material.

Browse courses on Model Deployment

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Find online tutorials on model deployment and optimization
Follow the tutorials and complete the exercises

Create a model pipeline

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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

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Writing a blog post will help you synthesize and solidify your understanding of the material.

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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

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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

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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:

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Quantitative 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.

See salaries and explore the career path for Risk 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.

See salaries and explore the career path for Financial 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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Database Administrator

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.

See salaries and explore the career path for IT Project Manager

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.

See salaries and explore the career path for Software Tester

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.

Machine Learning Engineering

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A concise guide to deploying a machine learning model into production. It covers topics such as model training, evaluation, deployment, and monitoring.

Machine Learning Engineering

Paperback

Machine Learning Engineering

Kindle Edition