Cohort 1

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

Valerii Babushkin

Senior Principal at BP, Kaggle  Grandmaster

Arseny Kravchenko

Staff Machine Learning Engineer, Kaggle Master

The Essentials of Machine Learning System Design

Facebook

Alibaba Group

Blockchain.com

Wargaming

Yandex

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&amp;A sessions to help you synthesize and apply the course material. You’ll develop:&nbsp; — 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.

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

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

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

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

This course is for:

What you’ll get out of this course

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

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

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.&nbsp;

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

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.

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

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.

Guidance for securing smooth integration and sustainable growth

Provide a brief description for each module as a way to sell the value of what students will be learning throughout the course.

First Module

Second Module

Third Module

Course syllabus

What people are saying

Meet your instructors

Oct 5 — Dec 8

20 modules stretched over 10 weeks

Questions trigger fruitful discussions, so speak up!

Live Q&A sessions to wrap up each module

Course schedule

This course builds on live workshops and hands-on projects

Active hands-on learning

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

Interactive and project-based

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

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