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Anatomy of a machine learning system design interview question

Machine learning system design interviews have become increasingly common as more industries adopt ML systems. While similar in some ways to generic system design interviews, ML interviews are different enough to trip up even the most seasoned developers. The most common problem is to get stuck or intimidated by the large scale of most ML solutions.

Today, we'll prepare you for your next ML system design interview by breaking down how they're unique, what you should do to prepare, and the 5 steps you should use to solve any ML problem.

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What's different about the ML system design interview?

The general setup of a Machine Learning System Design Interview is similar to a generic SDI. For both, you'll be placed with an interviewer for 45 to 60 minutes and be asked to think through the components of a program.

ML interviews generally focus more on the macro-level (like architecture, recommendation systems, and scaling) and avoid deeper design discussions on topics like availability and reliability.

In ML interviews, you'll be asked high-level questions on how you'd set up each component of a system (data gathering, ML algorithm, signals) to handle a heavy workload and still adapt quickly.

For the Machine Learning SDI, you'll be expected to explain how your program acquires data and achieves scalability.

How to prepare for an ML system design interview

An ML system design interview will test your knowledge of two things: your knowledge of the setups and design choices behind large-scale ML systems, and your ability to articulate ML concepts as you apply them.

Let's look at three ways to prepare both your knowledge and articulation.

Know the common ML interview questions

The best way to prepare for these questions is to practice ML SDI problems on your own. There are only a few types of ML interview questions asked in modern ML SDI's.

The most common are iterations of:

  • "Create a Twitter-style social media feed to display relevant posts to users."
  • "Build a recommendation system to suggest products/services."
  • "Design a Google-style search engine that tailors results to the user."
  • "Build an advertising system that presents personalized ads to users."
  • "Design an ML system identifies bad actors in a social network."

Search in the target job's description for mentions of specific systems you'd work with and study similar systems for the interview. For jobs without a clear leaning toward any question type, focus on the "media feed" and "recommendation" systems, as these are the two most asked questions.

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Focus on the 4 parts of every ML solution

Each ML solution has four major parts:

  • Machine learning algorithm
  • Training data
  • Signals (sometimes called features)
  • Validation and Metrics

For algorithms, what algorithm will you choose and why? Deep learning, linear regression, random forest? What are the strengths and weaknesses of each? What do they accomplish per your system's needs?

For data, where will you get test data? What data points will you draw from? How many data points will you handle?

For signals, what metric does your program use to determine relevant data? Will you signal to focus on one aspect of the data or synthesize it from multiple? How long does it take to determine data relevancy?

For metrics, what metrics will you track for success and program learning? How would you measure the success of your system? How will you validate your hypothesis?

Practice explaining out loud

Many interviewees will study concepts and algorithms but fail to practice the spoken component of the interview.

Practice explaining your system's architecture aloud throughout the process. Narrate any decisions you make, briefly explaining why you made that choice. This is a great opportunity to show the interviewer how you think, not just what you know.

Also, practice your answers to common probing questions. The interviewer will ask you to clarify any decision points or uncertainties in your program. Make sure you can justify the design choices you make at any point in the process.

Some common probe questions are:

  • How will this program perform at scale?
  • How will you acquire your training data?
  • What will you do to keep latency low?

Learn system design for Machine Learning interviews

Never be caught off guard by a machine learning question again. Get tips and solutions guides for each of the most asked ML interview questions, written by real industry interviewers.

Grokking the Machine Learning Interview

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.

So, generally, you'll be expected to cover all key areas of your ML program in 35 to 50 minutes. It's important to come with a structured plan of how you'll draft the system to ensure you stay on track.

Next, we'll look at how to break-up your time to ace any ML question. To help understand the process, we'll also demonstrate each step through an example feed-type question in a 45-minute interview.

You can adapt these steps to a 60-minute interview if you scale up the time of each step.

Our question is: Create a content feed to display personalized posts to users.

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Step 1: Clarify requirements (5 minutes)

For the first 5-minutes, we'll clarify our system goal and requirements with the interviewer. These interview questions are deliberately vague to make you directly ask for the information you'll need. Your clarifying questions will help steer your design and decide your system's end goal.

Some common clarifying questions would be:

  • How many users do we expect this program to handle?
  • What metrics are we currently tracking?
  • What do we want to achieve with this system? What do we want to optimize for?
  • What type of input do we expect?

Step 1: example

If we were clarifying the feed question, we'd ask:

  • What type of feed will this be? Purely text? Text and images?
  • How many users do we expect to have? How many posts does each make per day?
  • What metric does our system optimize for? Do we want more engagement per post or to increase the number of posts?
  • Do we have a target latency?
  • How quickly will our system apply new learning?

Step 2: High-level design (5 minutes)

For the next 5-minutes, create a high-level design that handles data from input to use. Chart this visually and connect all components that interact. The interviewer will ask probing questions as you build, so look out for questions that suggest you're missing a component.

Remember to keep this abstract: decide how many layers, how will data enter the system, how will data be parsed, and how will you decide relevant data?

Make sure to explicitly mention any choices you make for scalability or response time.

Step 2: example

We'd write that our training data is from our current social media platform. Fresh live data will enter the system each time a new post is created based on the creator's location, the popularity of the creator's past posts, and the accounts that follow that creator.

We'll use these metrics to determine how relevant a post is to a user. Relevancy will be determined when the app is launched. Our goal is to increase engagement per post.

Step 3: Data deep-dive (10 minutes)

For the next 10 minutes, take a deep-dive to explain your data. Make sure to cover both training data and live data. Think about how the data will need to transform through the process.

ML interviewers are looking for candidates who understand the importance of data sampling. You'll be expected to clarify where you'd get the training data, what data points you'd use present within the current system, and what data you want to begin tracking.

This differs from a generic SDI where the interviewee only considers what happens to the data after it enters the program flow.

For training data, consider:

  • What source will I get my training data from?
  • How will I ensure it is unbiased?

For live data, consider:

  • What signal will I use in my data?
  • Why is this signal relevant?
  • Are there any situations this signal would not reflect my desired outcome?
  • How reactive is my algorithm? Will changes occur within 10 minutes or 10 hours, etc?
  • How much data can my program handle at once? Does it perform worse with more input?

Step 3: example

We'll expect each user to follow 300 accounts and each account to make an average of 3 posts per day. We'll have three layers of data evaluation to keep latency low when the system evaluates the 1000 posts. The first layer quickly cuts a majority of posts based on the post-popularity.

The second layer uses locational data to cut posted based on locality, this is our second quickest layer. The third layer will be the longest and will cut posts using cross-engagement data between the follower and followed.

Step 4: Machine learning algorithms (10 minutes)

For the next 10 minutes, break down your choice of machine learning algorithm(s) to the interviewer. Each algorithm handles certain tasks differently, and the interviewer will want you to know the strengths and weaknesses of different algorithms. See our article for a quick guide on the top 10 ML algorithms.

If you use several algorithms to handle scale, mention how their results will factor together and your reasons for choosing multiple algorithms.

Make sure to mention how each algorithm utilizes your signals to create a cohesive solution. The same signal may not be as effective in one algorithm as it is in another.

Step 4: example

We'll use the feedforward neural network algorithm to predict relevancy. This algorithm works well with our creator/user interactions signal because it forms predictions off of non-circular connection webs.

Step 5: Experimentation (5 minutes)

In the final 5 minutes, stake a hypothesis of what your system will accomplish. This section is a sort of conclusion for your program where you can summarize how the components together achieve a certain goal.

Your hypothesis may be broad, like "posts ordered by relevance will get more engagement than chronological", or it may be specific, like "the addition of a location signal will increase engagement by 0.5%".

From here, explain how you'd test this hypothesis. Make sure to cover the initial offline evaluations and also how to evaluate online.

  • What offline evaluations would you use?
  • How big of an offline data set would you need?
  • When online, what will you do if there is a bug?
  • How will you track user satisfaction with the change?
  • Do you count engagement using comments or any form of interaction with the post?

ML engineers constantly test out hypotheses in their day-to-day. A focus on experimentation will set you apart from other applicants, as it shows that you can synthesize the functionality of your program and possess the right mindset for the job.

Step 5: example

Our relevancy-based feed will increase user engagement by 0.5%. We'll first use offline models programmed to simulate users and see what types of posts come through to the feed.

Once we move online, we'll track posts with the keyword "update" and "relevance" to determine effectiveness.

5-Step Summary:

  • Step 1: Clarify requirements (5 minutes)
  • Step 2: High-level design (5 minutes)
  • Step 3: Data deep-dive (10 minutes)
  • Step 4: Machine learning algorithms (10 minutes)
  • Step 5: Experimentation (5 minutes)

Wrapping up and resources

You now have everything you need to ace your next ML interview. By preparing ML study material and a timed solution plan, you'll set yourself apart from others still unfamiliar with this rising interview type.

To help you study up on ML algorithms and concepts, we've created the course Grokking the Machine Learning Interview to simplify the process and maximize your preparation. This course is designed to be your one-stop study material for ML interviews, including all the most tested concepts and step-by-step solutions to top interview questions.

Happy interviewing!

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