Ml System Design Questions

A booklet on machine learning systems design with exercises. A booklet on machine learning systems design with exercises. Add link to exercise answers. Seeing the recent requirements in big tech companies for mle roles and our confusion around it, i decided to create a framework for solving any ml system design problem during the interview. My note might be a bit too big for the 45 minute duration of the interview. Depending on your expertise and interviewers guide, you might want to emphasize. Most ml system design interview questions can be answered following the template below. One of the trickiest interview rounds for ml practitioners is ml systems design.

Add link to exercise answers. Seeing the recent requirements in big tech companies for mle roles and our confusion around it, i decided to create a framework for solving any ml system design problem during the interview. My note might be a bit too big for the 45 minute duration of the interview. Depending on your expertise and interviewers guide, you might want to emphasize. Most ml system design interview questions can be answered following the template below. One of the trickiest interview rounds for ml practitioners is ml systems design. If you’re applying to be a data scientist, ml engineer or ml manager at a big tech company, you’ll probably face an ml systems design question. I recently tackled this question at a few big tech companies on my way to becoming a staff ml engineer at pinterest. 5 steps to solve any ml system design problem. An ml sdi interview will usually have a strict time limit of either 45 or 60 minutes, with 5 minutes at the start and end for introductions/wrap up.

A booklet on machine learning systems design with exercises. Add link to exercise answers. Seeing the recent requirements in big tech companies for mle roles and our confusion around it, i decided to create a framework for solving any ml system design problem during the interview. My note might be a bit too big for the 45 minute duration of the interview. Depending on your expertise and interviewers guide, you might want to emphasize. Most ml system design interview questions can be answered following the template below. One of the trickiest interview rounds for ml practitioners is ml systems design. If you’re applying to be a data scientist, ml engineer or ml manager at a big tech company, you’ll probably face an ml systems design question. I recently tackled this question at a few big tech companies on my way to becoming a staff ml engineer at pinterest.

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Video Gallery For Ml System Design Questions

ML System Design: Feature Store



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Additional References:
medium.com/data-for-ai/comprehensive-and-comparative-list-of-feature-store-architectures-for-data-scientists-and-big-data-86ea8c4d853b

[1/5] How to Answer ML System Design Interview Questions



System design interviews can be tricky because they are open ended, ambiguous, and there is no single correct answer.

Here’s a recipe (part 1/5) for approaching these ambiguous questions in a structured manner and demonstrate to the interviewer that you can design a great ML system 🔥

Best of luck! 🙌

———

Connect with me on Instagram:
📷 instagram.com/samia.kld/

AWS re:Invent 2020: Designing better ML systems: Learnings from Netflix



Data science at Netflix goes far beyond eponymous recommendation systems and touches every aspect of the business, from optimizing content delivery to fighting fraud. Netflix’s unique culture affords its data scientists extraordinary freedom of choice in tools, which results in an ever-expanding set of machine learning (ML) approaches and systems. In 2019, Netflix open-sourced Metaflow, its human-centric ML platform. In this session, Netflix shares some lessons learned in its multi-year journey building the ML systems that Metaflow incorporates, covering a diverse range of scale from one-time experimentation on laptops to large-scale model training and serving systems on AWS.

Learn more about re:Invent 2020 at bit.ly/3c4NSdY

Subscribe:
More AWS videos bit.ly/2O3zS75
More AWS events videos bit.ly/316g9t4

#AWS #AWSEvents

Выпуск 1: ML System Design собеседование с Валерием Бабушкиным



Ссылка на курс HARD ML: bit.ly/2WHgOmm

Собеседования – это всегда стресс, а собеседование на вакансию, где необходимо вспомнить все имеющиеся знания – стресс вдвойне. Так мы пришли к идее проведения открытых интервью, где сами задаём ситуацию и предполагаемую должность.

Поэтому мы вместе с Валерием Бабушкиным решили провести три открытых собеседования по ML System Design, а потом разобрать их с коллегами.

Учитесь Data Science с нами: karpov.courses/

0:00 Интро
1:59 Самое важное в ML Design
5:52 Тема интервью
6:28 Социальная сеть как источник заработка
7:14 Начало ответа
9:42 Первый комментарий
10:10 Отбор баннеров
10:51 Параметры баннеров
11:45 Второй комментарий
15:15 Как измерять модель
15:54 Выбор метрики
16:58 Третий комментарий
18:35 Вопрос про A/B-тестирование
20:45 Четвертый комментарий
22:37 Увеличиваем число моделей и инженеров
24:05 Пятый комментарий
25:17 Офлайн обучение
25:26 Шестой комментарий
27:23 Взаимодействие с баннером
30:15 Метрики ранжирования
33:09 Виды «не кликов»
34:12 Возврат к отбору баннеров
35:49 Седьмой комментарий
38:30 Вопрос переобучения
41:06 Деление обучающей выборки
47:38 Возврат к вопросу переобучения
52:18 Фидбек от всех
55:43 Обратная связь от Валеры

Machine Learning Course - 23. ML Design Pattern - Ranking



A full university-level machine learning course - for free. New lectures every week.

Designed as a first course for engineers, program managers, and data professionals who want to learn: the details of important machine learning algorithms; professional model building; and design patterns for building practical machine learning systems.

This lecture covers: Machine learning case study, ranking machine learning, mean average precision, ranking algorithm, ranknet.

And you can view the entire course in this playlist: youtube.com/playlist?list=PLrQmbzbRJ5mwDinvDEJ5B-KDZlPM-sCYO

Learn more (and find slides for this lecture) at: livingmachinelearning.com/course.html

Live on 17th July: ML System design Interview session



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ML Ops System Design feat Comet ML CEO Gideon Mendels | Stanford MLSys #44



Episode 44 of the Stanford MLSys Seminar Series!

MLOps System Design for Development and Production
Speaker: Gideon Mendels

Abstract:
While ML model development is a challenging process, the management of these models becomes even more complex once they're in production. Shifting data distributions, upstream pipeline failures, and model predictions impacting the very dataset they’re trained on can create thorny feedback loops between development and production. In this talk, we’ll examine some naive ML workflows that don’t take the development-production feedback loop into account and explore why they break down. Next, we'll showcase some system design principles that will help manage these feedback loops more effectively. Finally, we’ll examine several industry case studies where teams have applied these principles to their production ML systems.

--

Stanford MLSys Seminar hosts: Dan Fu, Karan Goel, Fiodar Kazhamiaka, and Piero Molino
Executive Producers: Matei Zaharia, Chris Ré

Twitter:
twitter.com/realDanFu​
twitter.com/krandiash​
twitter.com/w4nderlus7

Intro music:
Heading Home by Nekzlo @nekzlo
Music provided by Free Music for Vlogs youtu.be/YGL35p7LLqo

--
0:00 Starting soon
10:21 Presentation
34:24 Discussion

Check out our website for the schedule: mlsys.stanford.edu
Join our mailing list to get weekly updates: groups.google.com/forum/#!forum/stanford-mlsys-seminars/join

#machinelearning #ai #artificialintelligence #systems #mlsys #computerscience #stanford #cmu #optimizingml #optimization #automaticoptimization

[3/5] How to Answer ML System Design Interview Qs



System design interviews can be tricky because they are open ended, ambiguous, and there is no single correct answer.

Here’s a recipe (part 3/5) for approaching these ambiguous questions in a structured manner and demonstrate to the interviewer that you can design a great ML system 🔥

Lmk in the comments if you would like me to do a detailed video on this topic.

Best of luck! 🙌

----------------

Let's connect:
📷 Instagram: instagram.com/samia.kld/
👩‍💻 LinkedIn: linkedin.com/in/samiakhalid/

Who am I?
🧠 Applied Artificial Intelligence Engineer at Microsoft
📹 I mostly create videos about learning topics that are not commonly taught but are really important for the future ahead of us and for living the ultimate life
🔥 I'm passionate about fitness, reading, adventures and living a life rich in experiences
👩‍🏫 My Machine Learning Blog: towardsml.com/

Principles Of Good ML Systems Design



💻 Abstract:
Principles Of Good ML Systems Design. This talk covers what it means to operationalize ML models. It starts by analyzing the difference between ML in research vs. in production, ML systems vs. traditional software, as well as myths about ML production. It then goes over the principles of good ML systems design and introduces an iterative framework for ML systems design, from scoping the project, data management, model development, deployment, maintenance, to business analysis. It covers the differences between DataOps, ML Engineering, MLOps, and data science, and where each fits into the framework. It also discusses the main skills each stage requires, which can help companies in structuring their teams. The talk ends with a survey of the ML production ecosystem, the economics of open source, and open-core businesses.

🔊 Speaker bio:
Snorkel AI - Machine Learning Engineer & Open Source Lead
Chip Huyen works to bring the best practices to machine learning production. She’s built AI applications at Snorkel AI, Netflix, NVIDIA, and Primer. She graduated from Stanford, where she taught TensorFlow for Deep Learning Research. She’s also the author of four bestselling Vietnamese books.

If you enjoyed this talk, visit us at mlopsworld.com/ and come participate in our next gathering! 💼

Would you like to receive email summaries of these talks? Join our newsletter FREE here: bit.ly/MLOps_Summaries 📧

Timestamps:

0:00 Intro
0:12 Getting to know Chip Huyen
2:47 Table of content
3:12 ML in research vs in productions
3:23 Objectives (Research)
3:53 Objectives (Production)
5:42 Computation of priority (Research)
5:57 Computation of priority (Production)
6:19 Difference between latency and throughput

ML Production Myths

9:52 Myth 1: Deploying is hard
11:33 Myth 2: You only deploy one or two ML models at a time
15:08 Myth 3: If we don't do anything, model performance remains the same
18:08 Myth 4: You won't need to update your models as much
20:22 Myth 5: Most ML engineers don't need to worry about scale
21:31 Myth 6: ML can magically transform your business overnight
23:24 Myth 7: Efficiency improves with maturity

Four Phases of Machining Adoptions

24:38 Phase 1: Before ML
26:15 Phase 2: Simplest ML models
28:25 Phase 3: Optimizing simple models
29:29 Phase 4: Don't be afraid of rules-based
30:00 Principles of good ML systems design

❓ Q&A section ❓

31:32 How do you track which versions of training features are used?
32:12 How do you check which version of training fishers are used?

34:30 Closing remarks

[2/5] ML System Design Interview Recipe #shorts



System design interviews can be tricky because they are open ended, ambiguous, and there is no single correct answer.

Here’s a recipe (part 2/5) for approaching these ambiguous questions in a structured manner and demonstrate to the interviewer that you can design a great ML system 🔥

Lmk in the comments if you would like me to do a detailed video on this topic.

Best of luck! 🙌

----------------

Let's connect:
📷 Instagram: instagram.com/samia.kld/
👩‍💻 LinkedIn: linkedin.com/in/samiakhalid/

Who am I?
🧠 Applied Artificial Intelligence Engineer at Microsoft
📹 I mostly create videos about learning topics that are not commonly taught but are really important for the future ahead of us and for living the ultimate life
🔥 I'm passionate about fitness, reading, adventures and living a life rich in experiences
👩‍🏫 My Machine Learning Blog: towardsml.com/

[4/5] ML System Design Interview Recipe #shorts



System design interviews can be tricky because they are open ended, ambiguous, and there is no single correct answer.

Here’s a recipe (part 4/5) for approaching these ambiguous questions in a structured manner and demonstrate to the interviewer that you can design a great ML system 🔥

Lmk in the comments if you would like me to do a detailed video on this topic.

Best of luck! 🙌

----------------

Let's connect:
📷 Instagram: instagram.com/samia.kld/
👩‍💻 LinkedIn: linkedin.com/in/samiakhalid/

Who am I?
🧠 Applied Artificial Intelligence Engineer at Microsoft
📹 I mostly create videos about learning topics that are not commonly taught but are really important for the future ahead of us and for living the ultimate life
🔥 I'm passionate about fitness, reading, adventures and living a life rich in experiences
👩‍🏫 My Machine Learning Blog: towardsml.com/

ML System Design Jam: Session Based Recommender System



A live system design session where we have a business stakeholder, who knows the business problem and the system in place, and engineering consultants who will collaborate to gather requirements and tackle the problem by designing the system that would support such a solution.

The breakout rooms are not recorded, so please do skip them.

For prep materials, please take a look at the past material in the playlist here: youtube.com/watch?v=BdY6rzYwFe0&list=PLLm69KFEX6JBMglmP7Ra_l_eJ2dlwIHQ1

Who we are: Members of an MLOps Discord server (lead by Chip Huyen). We discuss MLOps and engineering topics so we can learn together. We held a similar event to discuss "Designing Twitter's Trending Hashtags Solution".

Blog post here mlops-discord.github.io/blog/designing-twitters-trending-hashtags-solution/

The people you see on the call are part of a cohort. We will together go through background materials on RecSys then we will have the main jam session to design this system.

Join us on Discord ( discord.gg/WM4b7Q7nzp) to chat with speakers and many more brilliant folks

ML System Design Jam: Recsys Session 1 (Google’s intro to RecSys ) by Chloe He



An overview of Recommender systems and their components

This session serves as preparation material for the main jam session we will have on the June 19th, 2022.

Who we are: Members of an MLOps Discord server (lead by Chip Huyen). We discuss MLOps and engineering topics so we can learn together. We held a similar event to discuss "Designing Twitter's Trending Hashtags Solution".

Blog post here mlops-discord.github.io/blog/designing-twitters-trending-hashtags-solution/

The people you see on the call are part of a cohort. We will together go through background materials on RecSys then we'll have a main jam session to design this system.

Join us on Discord ( discord.gg/WM4b7Q7nzp) to chat with speakers and many more brilliant folks

Stanford MLSys Seminar Episode 0: ML + Systems



Introductory video to the Stanford MLSys Seminar Series. Join us every week Thursday at 1:30-2:30 PM PT!

Check out our website for our schedule and more details: mlsys.stanford.edu/
Subscribe and join our mailing list for weekly updates! groups.google.com/forum/#!forum/stanford-mlsys-seminars/join

Reference architectures for ML system design #shorts



ML System Design For a Product Search Engine | Live Session | Applied AI Course



ML System Design For a Product Search Engine | Live Session | Applied AI Course

#systemdesign #searcengine #appliedaicourse

--------------------------------------------------------------------------------------------------------------------------------------

The AppliedAICourse attempts to teach students/course-participants some of the core ideas in machine learning, data-science and AI that would help the participants go from a real world business problem to a first cut, working and deployable AI solution to the problem. Our primary focus is to help participants build real world AI solutions using the skills they learn in this course.

This course will focus on practical knowledge more than mathematical or theoretical rigor. That doesn't mean that we would water down the content. We will try and balance the theory and practice while giving more preference to the practical and applied aspects of AI as the course name suggests. Through the course, we will work on 20+ case studies of real world AI problems and datasets to help students grasp the practical details of building AI solutions. For each idea/algorithm in AI, we would provide examples to provide the intuition and show how the idea to used in the real world.

For more information, please visit: appliedaicourse.com/

For any queries you can either drop a mail to [email protected] or call us at +91 8106-920-029 or +91 6301-939-583

Facebook: facebook.com/appliedaicourse
Soundcloud: soundcloud.com/applied-ai-course

Public LIVE: Architecture and Design of Distributed ML systems



Announcement: youtu.be/W5691uLVegc

Recommendation Engine Design Deep Dive with Google SWE! | Systems Design Interview Question 20



There should be an engine that only has one recommendation to get you sum bitches

If you're curious about this topic and the ML side, highly recommend the talks by Eugene Yan about ML systems design on YouTube - I heavily plagiarized from him for this

00:00 Introduction
00:58 Functional Requirements
01:44 Capacity Estimates
02:45 API Design
03:08 Database Schema
03:50 Architectural Overview

Designing Better ML Systems: Learnings from Netflix



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Uncover emerging software trends and practices to solve your complex engineering challenges, without the product pitches. QCon San Francisco brings together the world's most innovative senior software engineers, architects and team leads across multiple domains to share their real-world implementation of emerging trends and practices.

Save your spot now: bit.ly/3MA2tgN
----------------------------------------------------------------------------------------------------------------
Video with transcript included: bit.ly/2VVi05t

Savin Goyal shares lessons learned by Netflix building their ML infrastructure, and some of the tradeoffs to consider when designing or buying a machine learning system.

This presentation was recorded at QCon Plus 2020: bit.ly/3pfdF6I

#Netflix #DataScience #MachineLearning

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