MLOps for Scaling TinyML from edX

Are you ready to scale your (tiny) machine learning application? Do you have the infrastructure in place to grow? Do you know what resources you need to take your product from a proof-of-concept algorithm on a device to a substantial business?

Machine Learning (ML) is more than just technology and an algorithm; it's about deployment, consistent feedback, and optimization. Today, more than 87% of data science projects never make it into production. To support organizations in coming up to speed faster in this critical domain it is essential to understand Machine Learning Operations (MLOps). This course introduces you to MLOps through the lens of TinyML (Tiny Machine Learning) to help you deploy and monitor your applications responsibly at scale.

MLOps is a systematic way of approaching Machine Learning from a business perspective. This course will teach you to consider the operational concerns around Machine Learning deployment, such as automating the deployment and maintenance of a (tiny) Machine Learning application at scale. In addition, you’ll learn about relevant advanced concepts including neural architecture search, allowing you to optimize your models' architectures automatically; federated learning, allowing your devices to learn from each other; and benchmarking, enabling you to performance test your hardware before pushing the models into production.

This course focuses on MLOps for TinyML (Tiny Machine Learning) systems, revealing the unique challenges for TinyML deployments. Through real-world examples, you will learn how tiny devices, such as Google Homes or smartphones, are deployed and updated once they’re with the end consumer, experiencing the complete product life cycle instead of just laboratory examples.

Are you ready for a billion users?

What's inside

Learning objectives

Know why and when deploying mlops can help your (tiny) product or business
Key mlops platform features that you can deploy for your data science project

How to automate a mlops life cycle
Real-world examples and case studies of mlops platforms targeting tiny devices

Know why and when deploying mlops can help your (tiny) product or business
Key mlops platform features that you can deploy for your data science project
How to automate a mlops life cycle
Real-world examples and case studies of mlops platforms targeting tiny devices

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides business perspective on deploying Machine Learning with MLOps, addressing operational concerns

Practical focus on TinyML (Tiny Machine Learning), catering to the unique challenges of deploying on tiny devices

Emphasizes the full product lifecycle, covering deployment and updates after reaching end consumers

Introduces advanced concepts like neural architecture search, federated learning, and benchmarking

Taught by experienced instructors Vijay Janapa Reddi and Dr. Larissa Suzuki

May require prior knowledge in Machine Learning and related concepts

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 MLOps for Scaling TinyML with these activities:

Review fundamental concepts of machine learning.

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This activity will refresh your knowledge of foundational machine learning concepts.

Browse courses on Machine Learning

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Review supervised and unsupervised learning algorithms.
Practice applying machine learning techniques to sample datasets.

Review Python for TinyML applications.

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This activity will review and prepare you for using Python for TinyML applications.

Browse courses on Python

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Review the basics of Python syntax.
Practice writing Python code for data manipulation and analysis.
Explore Python libraries for TinyML.
Build a simple TinyML model using Python.

Apply TinyML algorithms to real-world datasets.

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This activity will strengthen your understanding of TinyML algorithms through practical application.

Browse courses on TinyML

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Select a real-world dataset.
Choose and apply appropriate TinyML algorithms to the dataset.
Evaluate the performance of the algorithms.

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Collaborate with peers to develop TinyML solutions for real-world challenges.

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This activity will enhance your problem-solving skills and teamwork abilities.

Browse courses on TinyML

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Join or form a peer group.
Identify a real-world challenge suitable for TinyML.
Collaborate with peers to develop a TinyML solution.
Present your solution to the group.

Explore advanced topics in TinyML, such as neural architecture search and federated learning.

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This activity will introduce you to cutting-edge TinyML techniques.

Browse courses on Neural Architecture Search

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Identify advanced TinyML topics of interest.
Seek out and follow relevant tutorials.
Experiment with the techniques and apply them to TinyML projects.

Participate in a TinyML hackathon or competition.

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This activity will test your skills and accelerate your learning through hands-on challenges.

Browse courses on TinyML

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Identify suitable TinyML hackathons or competitions.
Form a team or participate individually.
Develop and submit your TinyML solution.
Receive feedback and learn from the experience.

Design a TinyML system for a specific application.

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This activity will challenge you to apply your knowledge of TinyML to design a complete system.

Browse courses on TinyML

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Identify a specific application for TinyML.
Determine the requirements and constraints of the application.
Select and integrate appropriate hardware and software components.
Develop and test the TinyML model.
Deploy and evaluate the TinyML system.

Develop a TinyML-powered application from scratch.

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This activity will provide you with a comprehensive learning experience by taking you through the entire development process.

Browse courses on TinyML

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Identify a problem or opportunity that can be addressed with TinyML.
Design and develop the TinyML model.
Implement the model into a functional application.
Deploy and evaluate the application.

Career center

Learners who complete MLOps for Scaling TinyML will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers focus on implementing the mathematics and theory of machine learning to design and build ML models. This course will be useful for a Machine Learning Engineer who wants to learn more about MLOps. It can help build a foundation for understanding how to deploy and monitor machine learning applications at scale.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use machine learning models to analyze and interpret data. This course may be helpful for a Data Scientist who is interested in learning more about MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, test, and maintain software systems. This course may be useful for a Software Engineer who wants to gain experience in MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale.

See salaries and explore the career path for Software Engineer

DevOps Engineer

DevOps Engineers work to bridge the gap between development and operations teams. This course may be useful for a DevOps Engineer who wants to learn more about MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale.

See salaries and explore the career path for DevOps Engineer

Product Manager

Product Managers are responsible for the overall vision and development of a product. This course may be useful for a Product Manager who wants to learn more about MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale, which can be valuable for managing a product that relies on ML.

See salaries and explore the career path for Product Manager

Data Analyst

Data Analysts collect, analyze, and interpret data to make informed decisions. This course may be useful for a Data Analyst who is interested in learning more about MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale, which can be valuable for analyzing data from ML models.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts analyze business processes to identify inefficiencies and opportunities for improvement. This course may be useful for a Business Analyst who wants to learn more about MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale, which can be valuable for analyzing the impact of ML on a business.

See salaries and explore the career path for Business Analyst

Project Manager

Project Managers plan, organize, and execute projects. This course may be useful for a Project Manager who is leading a project that involves the deployment of machine learning applications. It can provide an understanding of how to deploy and monitor machine learning applications at scale.

See salaries and explore the career path for Project Manager

Data Architect

Data Architects design and build data architectures. This course may be useful for a Data Architect who wants to learn more about MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale, which can be valuable for designing an architecture that supports ML.

See salaries and explore the career path for Data Architect

Database Administrator

Database Administrators manage and maintain databases. This course may be useful for a Database Administrator who wants to learn more about MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale, which can be valuable for managing a database that supports ML.

See salaries and explore the career path for Database Administrator

System Administrator

System Administrators manage and maintain computer systems. This course may be useful for a System Administrator who wants to learn more about MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale, which can be valuable for managing a system that supports ML.

See salaries and explore the career path for System Administrator

Network Administrator

Network Administrators manage and maintain computer networks. This course may be useful for a Network Administrator who wants to learn more about MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale, which can be valuable for managing a network that supports ML.

See salaries and explore the career path for Network Administrator

Cybersecurity Analyst

Cybersecurity Analysts protect computer systems and networks from cyberattacks. This course may be useful for a Cybersecurity Analyst who wants to learn more about MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale, which can be valuable for protecting a system that supports ML.

See salaries and explore the career path for Cybersecurity Analyst

Quality Assurance Analyst

Quality Assurance Analysts test and evaluate software to ensure that it meets quality standards. This course may be useful for a Quality Assurance Analyst who wants to learn more about MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale, which can be valuable for testing ML applications.

See salaries and explore the career path for Quality Assurance Analyst

Technical Writer

Technical Writers create and maintain documentation for software and other technical products. This course may be useful for a Technical Writer who wants to learn more about MLOps. It can provide an understanding of how to deploy and monitor machine learning applications at scale, which can be valuable for writing documentation for ML products.

See salaries and explore the career path for Technical Writer

MLOps for Scaling TinyML

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