The Essentials of Machine Learning System Design

4.5

(8 ratings)

·

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.

This course includes

20 interactive live sessions

Lifetime access to course materials

66 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
  • Week 1

    Oct 5—Oct 6

    Events

    • Oct

      5

      Session 1: Intro, Problem vs Solution Space

      Sat, Oct 5, 3:00 PM - 4:30 PM UTC

    • Oct

      6

      Session 2: Design Document

      Sun, Oct 6, 3:00 PM - 4:30 PM UTC

    Modules

    • Is there a problem?

    • Preliminary research

    • Design document

  • Week 2

    Oct 7—Oct 13

    Events

    • Oct

      12

      Session 3: Loss Function and Metrics

      Sat, Oct 12, 3:00 PM - 4:30 PM UTC

    • Oct

      13

      Session 4: Datasets

      Sun, Oct 13, 3:00 PM - 4:30 PM UTC

    Modules

    • Loss functions and metrics

    • Datasets

  • Week 3

    Oct 14—Oct 20

    Events

    • Oct

      19

      Session 5: Validation Schemas

      Sat, Oct 19, 3:00 PM - 4:30 PM UTC

    • Oct

      20

      Session 6: Baseline Solution

      Sun, Oct 20, 3:00 PM - 4:30 PM UTC

    Modules

    • Validation schemas

    • Baseline solution

  • Week 4

    Oct 21—Oct 27

    Events

    • Oct

      26

      Session 7: Error Analysis Part 1

      Sat, Oct 26, 3:00 PM - 4:30 PM UTC

    • Oct

      27

      Session 8: Error Analysis Part 2

      Sun, Oct 27, 4:00 PM - 5:30 PM UTC

    Modules

    • Error analysis, part 1

    • Error analysis, part 2

  • Week 5

    Oct 28—Nov 3

    Events

    • Nov

      2

      Session 9: Training Pipelines

      Sat, Nov 2, 4:00 PM - 5:30 PM UTC

    • Nov

      3

      Session 10: Features and Feature Engineering

      Sun, Nov 3, 4:00 PM - 5:30 PM UTC

    Modules

    • Training pipelines

    • Features and feature engineering

  • Week 6

    Nov 4—Nov 10

    Events

    • Nov

      9

      Session 11: Measuring and Reporting Results

      Sat, Nov 9, 4:00 PM - 5:30 PM UTC

    • Nov

      10

      Session 12: Integration

      Sun, Nov 10, 4:00 PM - 5:30 PM UTC

    Modules

    • Measuring and reporting results

    • Integration

  • Week 7

    Nov 11—Nov 17

    Events

    • Nov

      16

      Session 13: Monitoring and Reliability Part 1

      Sat, Nov 16, 4:00 PM - 5:30 PM UTC

    • Nov

      17

      Session 14: Monitoring and Reliability Part 2

      Sun, Nov 17, 4:00 PM - 5:30 PM UTC

    Modules

    • Monitoring and reliability, part 1

    • Monitoring and reliability, part 2

  • Week 8

    Nov 18—Nov 24

    Events

    • Nov

      23

      Session 15: Serving and Inference Optimization

      Sat, Nov 23, 4:00 PM - 5:30 PM UTC

    • Nov

      24

      Session 16: Ownership and Maintenance

      Sun, Nov 24, 4:00 PM - 5:30 PM UTC

    Modules

    • Serving and inference optimization

    • Ownership and maintenance

  • Week 9

    Nov 25—Dec 1

    Events

    • Nov

      30

      Session 17: Creating a Design Document Part 1

      Sat, Nov 30, 4:00 PM - 5:30 PM UTC

    • Dec

      1

      Session 18: Creating a Design Document Part 2

      Sun, Dec 1, 4:00 PM - 5:30 PM UTC

    Modules

    • Creating Your Own Design Document

    • Writing a design document

  • Week 10

    Dec 2—Dec 8

    Events

    • Dec

      7

      Session 19: Presenting a Design Document

      Sat, Dec 7, 4:00 PM - 5:30 PM UTC

    • Dec

      8

      Session 20: Polishing a Design Document

      Sun, Dec 8, 4:00 PM - 5:30 PM UTC

    Modules

    • Publishing Your Own Design Document

    • Presenting a design document

4.5

(8 ratings)

What students are saying

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

MLSD_2nd Cohort

$1,400

Dates

Oct 5—Dec 8, 2024

Application Deadline

Sep 30, 2024
|

Bulk purchases

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

How long will the course take?

I work full-time, what is the expected time commitment?

Will this course teach me how I can ace job interviews?

How much time should I expect to spend on the course?

What deliverables/outcomes can I expect to walk away with?

Who is this course not for?

Is there a refund policy?

A pattern of wavy dots

Join an upcoming cohort

The Essentials of Machine Learning System Design

MLSD_2nd Cohort

$1,400

Dates

Oct 5—Dec 8, 2024

Application Deadline

Sep 30, 2024
|

Bulk purchases

$1,400

4.5

·

10 Weeks