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The Essentials of Machine Learning System Design

4.5 (8)

·

10 Weeks

·

Cohort-based Course

A comprehensive step-by-step guide designed to help you work on your ML system, from preliminary steps to deployment and maintenance.

Previously at

Facebook
Alibaba Group
Blockchain.com
Wargaming
Yandex

Course overview

Learn how to build and maintain robust, durable ML systems that bring value

ML System Design is a new area in machine learning that deserves to become a separate discipline. While there are plenty of books and courses that cover specific aspects of machine learning, there is scarce literature on the overall landscape of ML system design. Even among highly experienced ML practitioners, there’s a lack of a holistic perspective. Join other specialists seeking to level out these knowledge gaps, and learn directly from two experts in ML and data science with over 20 years of combined experience.


This course introduces machine learning system design as a unified pool of knowledge. We’ve developed a comprehensive framework covering all fundamental aspects of ML system design, and we’ll provide step-by-step guidelines and insights helpful to both novices and experts.


Course highlights:


60+ lessons on ML system design, including interactive sessions and practical advice.

— Two use cases with real-life scenarios.

Stories of wins and failures from our personal experiences.

Live Q&A sessions to help you synthesize and apply the course material.


You’ll develop: 


— A comprehensive knowledge of designing, training, deploying, and maintaining ML systems.

— The ability to confidently implement what you have learned in a real-world environment.

Hands-on experience that can be shared with colleagues.

This course is for:

01

Mid-career engineers: to hone their skills in building and maintaining solid ML systems and make sure they don’t miss anything critical.

02

Engineering managers and senior engineers: to fill the gaps in their knowledge and view ML system design from a broader perspective.

03

Those starting their journey in machine learning: to have structured guidelines at hand before kicking off their first ML project.

What you’ll get out of this course

A better understanding of your system’s problem space and solution space

You will increase overall awareness of the problem your system needs to solve and define the required steps before system development has started.

Deeper knowledge of the early-stage work of developing an ML system

You will learn more about the importance of picking the right metrics and loss functions, assembling a healthy data pipeline, combining various validation techniques, and preparing the earliest viable version of your future model. 

Skills to shape your system into a solid, accurate, and reliable solution

You will strengthen your skills in conducting error analysis, training your pipelines, engineering and evaluating feature sets for your model, and handling testing to evaluate the performance of your system.

Guidance for securing smooth integration and sustainable growth

You will discover the key practices of integrating your solution into the existing ecosystem, the nuances of model monitoring, the challenges of deployment optimization, and the importance of proper maintenance to make your system reliable, manageable, and future-proof.

What’s included

Live sessions

Learn directly from Valerii Babushkin & Arseny Kravchenko in a real-time, interactive format.

Lifetime access

Go back to course content and recordings whenever you need to.

Community of peers

Stay accountable and share insights with like-minded professionals.

Certificate of completion

Share your new skills with your employer or on LinkedIn.

Maven Guarantee

This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.

Course syllabus

Week 1

Oct 5—Oct 6

    Is there a problem?

    5 items

    Preliminary research

    4 items

    Design document

    3 items

Week 2

Oct 7—Oct 13

    Loss functions and metrics

    2 items

    Datasets

    6 items

Week 3

Oct 14—Oct 20

    Validation schemas

    4 items

    Baseline solution

    5 items

Week 4

Oct 21—Oct 27

    Error analysis, part 1

    2 items

    Error analysis, part 2

    2 items

Week 5

Oct 28—Nov 3

    Training pipelines

    5 items

    Features and feature engineering

    5 items

Week 6

Nov 4—Nov 10

    Measuring and reporting results

    3 items

    Integration

    4 items

Week 7

Nov 11—Nov 17

    Monitoring and reliability, part 1

    3 items

    Monitoring and reliability, part 2

    2 items

Week 8

Nov 18—Nov 24

    Serving and inference optimization

    4 items

    Ownership and maintenance

    3 items

Week 9

Nov 25—Dec 1

    Creating Your Own Design Document

    1 item

    Writing a design document

    1 item

Week 10

Dec 2—Dec 8

    Publishing Your Own Design Document

    1 item

    Presenting a design document

    1 item

4.5 (8 ratings)

What students are saying

It was an incredible opportunity to get SOTA knowledge about creating ML systems that must work in a production environment. Arseny and Valeriy did a great job sharing their experience and helping our group think about application in real life. So, if you want to connect your ML experience with creating complex systems or vice versa, this course will be a must.

Aliaksei

Cohort 1

Director of Engineering

No company

That was fun! There were many real-world examples to support the theoretical material, live discussions and knowledge sharing, not to mention the good sense of humor of both lecturers.

Artem

Cohort 1

Machine Learning Engineer

Outrider

"The Essentials of Machine Learning System Design" is a great starting point for those new to ML System design. The course offers a well-structured, step-by-step approach that guides users through the entire process, from defining the problem to deployment and maintenance. This comprehensive approach is particularly valuable for beginners who need a solid foundation in the core concepts. Overall, if you're new to ML system design and looking for a comprehensive introduction, it is a worthwhile course.

Hleb

Cohort 1

Data Scientist

The course was packed with a wealth of information, and the lecturers organized the material into well-structured segments. Each section of the course was thoughtfully crafted, making it easier to digest the presented information. Initially, I was concerned that the parts of the course where I already had hands-on experience might be redundant or boring. To my surprise, the diversity of the lecturers' experiences brought a fresh perspective, and I discovered many useful ideas and approaches that were new to me. This diversity was a significant strength of the course, ensuring that even experienced and seasoned professionals could gain valuable insights. Some sections were challenging, particularly those where I lacked hands-on experience. After such lectures, I had to spend additional time exploring the materials to better understand and structure the concepts in my mind. The accompanying book proved to be an excellent resource. It served as a helpful side material during the course and was invaluable for refreshing my memory on key topics. The book complemented the lectures perfectly and was a great reference tool. It's important to note that this course is not entry-level. To make the most of it, a solid professional background is essential. The content is advanced and assumes a certain level of prior knowledge and experience. The timeline for Cohort 1 was not enough to thoughtfully explore all sections with lecturers online, so there were some shortcuts. Overall, it is a comprehensive and somewhat challenging course that offers significant value to professionals in the field. The well-structured material, combined with the diverse experiences of the lecturers and the excellent accompanying book, makes it a highly recommendable course for those looking to deepen their understanding of Machine Learning System Design.

Pavel

Cohort 1

Data Scientist

IBA Group

I am very satisfied with the content and activities we has during the course. Both Arseni and Valery put a lot of effort to make the course engaging, the course lectures were fun! I learned a lot, and even more, many of the things we learned I already applied in practice. One thing that I missed a bit is broader discussions within a cohort students 🙂 but that does not depend on the course that much.

Hanna

Cohort 1

Machine Learning Engineer

self-employed

What people are saying

        It gives an excellent insight into the problems that a seasoned ML developer faces sooner or later. The case studies given during the theory drill are especially helpful because they allow you to build a picture of how the various design decisions are being made can affect the product and the business itself. Great job putting this together.
Reader review

Reader review

        While I am not new to ML system design, I was pleasantly surprised to find 30-40% of the content introducing fresh perspectives. Its brilliance isn't just in its new information but in its ability to structure and articulate knowledge in an easily digestible manner. Even for concepts I'm familiar with, it often reminds me of critical nuances.
Reader review

Reader review

        This book is an invaluable asset from industry veterans. It's rare to discover content that seamlessly integrates into daily work routines, but this does. Since my discovery, I use it practically every week and recommend it to all engineers in my team.
Reader review

Reader review

        Comprehensive and forthright explanations, expert insights, and practical examples make it a must-read!
Reader review

Reader review

Meet your instructors

Valerii Babushkin

Valerii Babushkin

Senior Principal at BP, Kaggle Grandmaster

Valerii is an accomplished data science leader with extensive experience in the tech industry. He currently serves as Head of Data, Analytics, and AI at BP, where he is responsible for leading the company's data-driven initiatives. Prior to joining BP, Valerii held key roles at leading tech companies, such as Facebook, Blockchain.com, Alibaba, and X5 Retail Group.

Arseny Kravchenko

Arseny Kravchenko

Staff Machine Learning Engineer, Kaggle Master

Arseny is a seasoned ML engineer with a proven track record of building and optimizing reliable ML systems for startups, including real-time video processing, manufacturing optimization, and financial transactions analysis.

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The Essentials of Machine Learning System Design

Course schedule

3-6 hours per week

  • Oct 5 — Dec 8

    Every Saturday and Sunday, 4 p.m. BST

  • 20 modules stretched over 10 weeks

    66 lessons overall

  • Live Q&A sessions to wrap up each module

    Questions trigger fruitful discussions, so speak up!

Free resource

🚀Win a Full Stipend🚀

We're thrilled to announce that the authors of the ML System Design Course are launching a full stipend opportunity for talented and passionate students!

To participate, all you need to do is share your story on LinkedIn by September 22nd. Tell us why you want to attend the course and how it will impact your journey. Don't forget to include the hashtag #SystemDesignMaven in your post!

Two winners will be announced on September 29th.

This is more than just a chance to win a stipend—it's an opportunity to showcase your passion for System Design!

Tell Us You're Participating

Learning is better with cohorts

Learning is better with cohorts

Active hands-on learning

This course builds on live workshops and hands-on projects

Interactive and project-based

You’ll be interacting with other learners through breakout rooms and project teams

Learn with a cohort of peers

Join a community of like-minded people who want to learn and grow alongside you

Frequently Asked Questions

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The Essentials of Machine Learning System Design