The Essentials of Machine Learning System Design

New
·

8 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

This course is popular

6 people enrolled last week.

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.

This course includes

16 interactive live sessions

Lifetime access to course materials

62 in-depth lessons

Direct access to instructor

Projects to apply learnings

Guided feedback & reflection

Private community of peers

Course certificate upon completion

Maven Satisfaction Guarantee

This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.

Course syllabus

Expand all modules

    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.

    A pattern of wavy dots
    Join an upcoming cohort

    The Essentials of Machine Learning System Design

    Cohort 1

    $800 USD

    Dates

    May 25—July 14, 2024

    Application Deadline

    May 22, 2024
    |

    Bulk purchases

    Course schedule

    3-6 hours per week
    • May 25 — July 14

      Every Saturday and Sunday, 4 p.m. UTC

    • 16 modules stretched over 8 weeks

      62 lessons overall

    • Live Q&A sessions to wrap up each module

      Questions trigger fruitful discussions, so speak up!

    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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    A pattern of wavy dots
    Join an upcoming cohort

    The Essentials of Machine Learning System Design

    Cohort 1

    $800 USD

    Dates

    May 25—July 14, 2024

    Application Deadline

    May 22, 2024
    |

    Bulk purchases

    $800 USD

    8 Weeks