Every engineer needs to understand the intersection of Machine Learning and System Design — for interviews and beyond.
It has been a big year for Machine Learning. Nearly every tech company (and many non-tech companies) with a viable AI use case is investing heavily in both ML engineering and product development.
Engineers who can design Machine Learning solutions at scale stand to win big. I highly recommend studying how ML solutions can support and enhance the large-scale distributed systems that underpin our modern tech landscape.
The intersection of Machine Learning and System Design is a fascinating one; every engineer will benefit from understanding the ways in which these two fields are becoming rapidly intertwined.
In today’s newsletter, I’ll touch on the four essential types of ML System Design Interview questions that developers should prepare for. Then I will provide a 4-step process for answering these types of questions effectively.
Before we jump in, I want to call out two of my favorite courses if you are ready to take your ML and System Design Interview skills to the next level:
ML enhances and optimizes various aspects of large-scale systems such as data analysis, predictive maintenance, security, and adaptability. Mastering the ML process will allow you to optimize solutions in System Design — as well as give you a leg up in design interviews.
This is a great prerequisite to designing distributed systems (in an interview setting and in real-world applications). Pairing these two courses is the most efficient way to ensure you’re up to date on what to expect in design interviews.
Now let’s start by unpacking the most important applications of ML in modern System Design.
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Let’s start with a quick refresher on Machine Learning (ML).
ML is a subset of AI that enables computers and machines to learn and make predictions without being explicitly programmed to do so. The learning process involves data collection, training, model evaluation, and then adjustments.
ML is important because it helps us make programs work effectively while solving real-world problems. Thoughtfully designed ML systems make everyday tasks easier, help businesses make smarter decisions, and open possibilities for progress and innovation across a variety of applications and industries.
ML System Design, on the other hand, enables us to deploy ML solutions at scale, unlocking their true potential and impact.
An example of ML System Design in action is the algorithmic ranking in platforms like Spotify, YouTube, TikTok, X (Twitter), and Instagram. These platforms use ML System Design to employ intricate ranking systems to tailor and optimize the content users encounter. The success of these platforms can be highly attributed to these ranking algorithms. They’re able to determine how relevant content is to your profile to keep you engaging with the platform.
A great real-world example is Twitter’s tweet ranker (see the diagram below).
In the realm of self-driving cars, ML System Design is used to enable safe and efficient autonomous driving. The algorithms are utilized to process vast amounts of information from sensors and cameras to recognize patterns, identify obstacles, and predict the behavior of other cars on the road.
Every industry (if it hasn’t already) is making proactive developments in hopes of adopting ML improvements and features. Accompanying this advancement is the demand for engineers prepared to deploy systems containing AI and ML elements.
As a result, a new component has been added to interviews: ML System Design questions. It's crucial for every candidate to have a clear understanding of what to anticipate, as these questions differ from typical SDI questions. Like any other System Design Interview, careful preparation is required.
ML Essentials: Here's what every dev should know
As a prerequisite to System Design Interviews, every engineer should have a basic understanding of the following:
ML terminology
Different branches of the data science field
Python libraries
Different types of ML systems
Uses of ML algorithms
After brushing up on the fundamentals, be sure to review how ML System Design falls into your area of interest. More granular specialization will depend on your personal career goals and objectives.
While ML-specific prompts share similarities with standard System Design Interview questions, they are generally seen as more challenging. ML System Design Interviews usually emphasize a broader macro design perspective rather than diving deep into intricate design topics.
What will you be asked in an ML System Design Interview?
The million-dollar question remains: “What questions will they actually ask me?”
However, the most efficient route to comprehensive interview prep isn’t to memorize individual questions — it’s to understand the central types of questions you will be asked.
Typically, there are four categories of ML Interview questions. Each one requires a certain degree of knowledge and preparation:
Algorithms and ML Theory
These questions are going to focus on how algorithms compare, as well as how to measure them accurately.
"Design a Google-style search engine that tailors results to the user.” or“Build a recommendation system to suggest products/services.”
Programming Skills
For this category, be prepared for an interviewer to inquire about Python or domain-specific languages.
“Create a Twitter-style social media feed to display relevant posts to users.”
Interest in Machine Learning
These types of questions require expertise on industry trends, as well as your vision for ML components of the future. Extensive preparation for this one is a must.
“Design an ML system that identifies bad actors in a social network.”
Industry or Product-specific Questions
Here you will see questions focused on how you take general ML knowledge and apply it to specific products.
“Build an advertising system that presents personalized ads to users.”
In a System Design portion of an ML interview, you’ll be tested on your knowledge of these solutions as well as your ability to articulate them. However, be prepared to receive a more open-ended problem, and work through how to build an ML system as a solution.
What it really comes down to is practicingproblem-solving, knowing how to optimize for performance, and being able to make informed decisions considering various trade-offs and practical implications.
How to answer an ML System Design interview question effectively
Once you become familiar with the types of questions you’ll be asked, it’s time to start an in-depth preparation of your responses. Regardless of the prompt, however, there are generally four steps to every ML System Design solution.
I’ll break them down here.
Start with the ML algorithm. This portion of the solution entails choosing an algorithm and then explaining or defending your choice. You’ll need to address the strengths and weaknesses, along with why your system will benefit from them.
Next is the data section. Here, you’ll need to focus on the logistics of your solution. Think about answers to questions such as: “Where will you get test data?” or “What data points will you draw from?” It’s essential to think these details through when building your response.
From here, the conversation should progress to signals. Identifying signals means creating heuristics that determine what data is relevant. Think about if your model is going to look at only one part of the data, or combine several parts. Assess how quickly the program will decide what’s relevant.
The last step of an ML solution is determining metrics. It’s essential to work out what things you’ll keep an eye on to ensure your program is doing well and learning. Be able to explain how you’ll measure success and validate your hypothesis.
Mastering this four-step approach guarantees an in-depth understanding of how to leverage ML and System Design in a limited time frame.
Final Takeaways
Remember to always practice your responses out loud. Even if you’re 100% confident in the answers themselves, there’s a big difference between knowing and articulating. These interview questions are going to test your confidence in both.
As you go through your responses, make sure you’re able to justify any step along the way. Ensure your solution is clear and concise — most interviewers will only allow up to 50 minutes for your response. Given the amount of material you're expected to cover, the ML System Design is extremely short. If you aren’t prepared, you could get caught off guard and achieve suboptimal performance.
Creating study materials for ML and devising a structured plan for timed solutions will help you distinguish yourself from those who aren’t familiar with this emerging interview format.
Design interviews are an essential metric for hiring managers to determine your level and salary. ML engineers command some of the biggest paychecks in tech, and there is often a great deal of offer money riding on one of these interviews. However, sooner rather than later, every engineer (not just ML engineers) will be expected to exhibit at least a base-level understanding of ML fundamentals and applications. The time to start upskilling is now.
We’ve seen a lot of changes in the tech hiring market in the past year, including the massive onset of AI and instability in the economy. Let’s talk about how to approach these changes in your job search.
This virtual event from our partners at Hired is your direct line to career experts and top companiesactively hiring tech talent. Network with employers and discover how to align your skills with their vision. Learn how to compete in interviews and negotiate salaries in a competitive market.
