In 2022, LinkedIn ranked machine learning engineer as the fourth fastest-growing job title in the past five years. And according to Precedence Research, the AI market cap will skyrocket to over $1.5 trillion by the early 2030s! 

All in all, it’s clear that ML is a rising role, making it the perfect time to land a job in the sector.

At AI/ML, we’re keen to help you land your first (or next) job in this revolutionary field. In today’s article, we’ll look at how you can ace your ML system design interview and explain why employers are so keen to evaluate your skills and knowledge in this area.

Let’s begin!

What is a machine learning system design interview?

Machine learning engineers will likely face a few rounds of interviews on their path to employment—one common one being a machine learning systems design interview.

The design of an ML system consists of setting up the project, constructing data pipelines, creating models, and training algorithms. ML system design interviews are equally multi-faceted—usually requiring you to explain the end-to-end design of an ML system that satisfies a certain use case (e.g., predicting customer behavior).

Given the open-ended nature of many ML system design interview questions, it will probably feel closer to a conversation than an interview in the traditional sense.

What is the value of an ML system design interview for employers?

There are several key reasons why a company might want to perform an ML system design interview as part of the hiring process.

Understanding your overall skills

ML system design interviews will give your interviewers the chance to assess your ability to end-to-end design a machine learning system for a specific purpose. This will primarily help them understand your development skills and problem-solving capacity.

Analyzing your practical skills/knowledge

This interview is a unique opportunity to demonstrate your potential value to an employer by applying machine learning and practical skills to real-world problems.

Evaluating your capacity to find technical solutions for abstract problems

The ML system design interview assesses how well you can develop technical solutions to abstract challenges. The interview evaluates how you might tackle real-world problems that you’d encounter in the role if hired.

How to ace an ML system design interview

In this section, we’ll explore some of the best tips to take on board for your interview.

Systematize your responses

While it’s impossible to know exactly what you’ll be asked in an ML systems design interview, it is possible to come up with a solid system for answering just about any question that comes up.

While the STAR method works well for behavioral interviews, ML systems design interviewees should use some variation of the CCAST method:

• (C) Clarify the question. The first step is to clarify what you are being asked. It’s important to understand exactly what the interviewers want you to do.
• (C) Collect data. Next, gather all relevant data.
• (A) Analyse data. Exploratory data analysis is key for deciding what model design will make most sense for the problem at hand.
• (S) Select a model. This is the part where you choose the most suitable design. 
• (T) Train the model. Finally, you must train your model to the best of your ability to address the problem at hand, effectively and efficiently.

When seeking answers to questions, you’ll need to balance structure with creativity so that your responses have as much value as possible.  Keep in mind that when responding to a problem, you should illustrate an array of relevant skills for the task and job at hand.

Study a range of patterns for ML system design 

Ahead of the interview, be sure to familiarize yourself with a wide range of machine learning system design patterns.

Common ML system design patterns include:

• Explainable Predictions: Developing reliable, accurate, and inclusive ML models can be difficult. This model introduces explainability to ML models so that engineers can see how they arrive at conclusions.
• Rebalancing: An imbalance in datasets is a common issue that can lead to suboptimal predictive performance. There are several strategies you can use to address this issue, such as choosing another performance metric, resampling, or using penalized learning algorithms to increase the cost of minority class misclassifications.
• Checkpoints: Checkpoints are snapshots of a model’s internal state, which can prove helpful as backup. By saving the model with the highest accuracy or creating checkpoints when any epoch ends, you can return to a prior system state in case of power outages, operating system faults, or other unexpected problems.
• Workflow Pipeline: The workflow pipeline is an approach designed to increase the scalability and ease of maintenance of the model. This design pattern has the purpose of isolating and containerizing the various steps involved in an ML’s workflow.
• Transform: This design pattern concerns distinguishing inputs from features. Inputs are usually not used as features in most machine learning problems. While many transformations would be applied to the input for feature conversion, reproducing these transformations at prediction time will cause issues. Therefore, it is important to differentiate clearly between your features and your inputs.

Of course, what design patterns will make the most sense for particular problems brought up in your interview will depend on the exact questions raised. When considering what design pattern to implement for a hypothetical ML system design, be sure to weigh up the pros and cons of utilizing the design patterns you have in mind before making an efficiently thought-out decision.

As you can tell, researching a whole host of ML system design patterns in advance may prove key to maximizing the hypothetical functionality and efficacy of your design when answering a question.

Hone your skills and knowledge ahead of the interview

Developing an understanding of a wide range of relevant concepts and sub-topics is a great way to prepare for your ML system design interview. 

Since many questions will be open-ended, broad knowledge and skills can prove very helpful when trying to impress your potential future employer. For all you know, diving more into the depths of AI and machine learning before your interview may even give you the edge needed to secure the role.

Practice, practice, practice!

Last, but not least, it’s essential that you practice with a few mock interview questions. Here are some examples that you can try to answer:

• Design a system that can recommend our services to subscribed users.
• Design an ML system to identify customer churn in a telecom company.
• Design an ML system to detect fraudulent transactions on online banking accounts.
• Design an ML system to predict future sales of a retail store.
• Explain how you’d build an entity recognizer system.

When answering practice questions consider scalability, reliability, storage, core systems, databases, and more. As touched on, the questions will probably be very open-ended.

Tips for the ML system design interview itself

Now, here are some key pieces of advice that apply to the day of the interview.

Clarify any requirements

During the interview, it is important to seek clarification for any requirements regarding system design. For the most part, your interviewers will keep their machine learning system questions vague as a way of encouraging you to ask for further clarity. You will likely be asked to design a system that achieves an objective presented in just a few words as part of the question, requiring clarity.

When seeking clarification, it’s important to understand the scope of the ML design you’re working on and any components that should be prioritized. 

It’s also necessary to rephrase the prompt in your own words to ensure that you understand exactly what they want from you for the question at hand. Knowing what to focus on and exactly what they’re asking is essential in answering the question most effectively.

Answer the “why” part of the question

When given a specific problem to solve with an ML system, be sure to answer the “why” embedded within the question. In other words, explain why your design solution would be an appropriate match for the issue at hand.

Conclusion

Today’s article looked at how systematization can help you structure your answers effectively (where applicable) and explored the best tips to prepare for and take into your interview.

If you’re tired of weeding through openings to machine learning positions, AI/ML can help. We feature only the best AI and ML careers from around the world, so you can focus on making your application perfect.

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