How do you decide when to use a pre-trained model versus training a model from scratch?

Machine Learning Engineer Interview Questions

Sample answer to the question

Well, when I'm deciding on using a pre-trained model versus training from scratch, I usually look at the project requirements first. If the task is a common one, like image recognition, where models like ResNet have been trained on millions of images, I'll go with a pre-trained one. It saves a lot of time and compute resources. But if the task is super specific, say, predicting machine failures in a unique manufacturing setting, I'll train a model from scratch to make sure it's tuned to the exact data we have.

A more solid answer

In my experience as a machine learning engineer, I've found that the decision to use a pre-trained model or train from scratch heavily depends on the data availability, project timeline, and the specificity of the task at hand. With projects where time and data are scarce, leveraging a pre-trained model affords efficiency and often suffices. For instance, while working on an image classification project at my last job, I utilized a pre-trained VGG16 model, which significantly shortened the development cycle. However, for niche problems with ample data, such as a predictive maintenance system I developed using sensor data, training a model from scratch allowed for a highly tailored solution that surpassed generic models in accuracy.

Why this is a more solid answer:

The solid answer includes a more comprehensive approach, considering factors such as data availability and project timeline. It provides specific examples of past work where the candidate applied both strategies, showcasing their experience and proficiency with ML libraries and frameworks. It also demonstrates a solid understanding of when it's beneficial to train a model from scratch, relating to experiences with data modeling. However, it still could delve deeper into the technical rationale for each decision and how the candidate's role in past projects directly contributed to their success.

An exceptional answer

When faced with the choice of using a pre-trained model or developing one from scratch, I apply a systematic approach guided by my extensive experience with machine learning techniques. For instance, in my role at XYZ Corp, I assessed the trade-offs concerning the anticipated performance gains, data specificity, and computational costs. Choosing a pre-trained model like InceptionV3 for standard image classification tasks allowed us to capitalize on the extensive work done in the domain, like leveraging features learned from ImageNet. However, when I spearheaded the development of a language model for legal document analysis, the specificity of the legal jargon mandated training from scratch. My deep understanding of RNNs and attention mechanisms, along with a proficiency in Tensorflow, enabled us to architect a model that not only met but exceeded our precision benchmarks. In each case, my decisions were underpinned by a blend of empirical studies, domain expertise, and knowledge of current ML frameworks, which ensured the models were robust and fit for purpose.

Why this is an exceptional answer:

The exceptional answer details a methodical approach to decision-making, incorporating considerations of performance gains, data specificity, and computational costs. The candidate references specific projects and technologies, such as InceptionV3 and RNNs, showing a high level of expertise and familiarity with advanced machine learning techniques and algorithms. Moreover, it conveys the candidate's leadership in developing tailor-made solutions and their ability to exceed benchmarks by leveraging their substantial machine learning experience and programming proficiency, thus meeting the job’s demands for strong analytical, problem-solving skills, and collaboration.

How to prepare for this question

Review past projects you've worked on, especially those where you had to choose between pre-trained models and training from scratch. Be prepared to discuss specific details such as the nature of the project, data used, time constraints, and your process of evaluating which approach to take.
Brush up on the use of pre-trained models in your field, and familiarize yourself with the latest machine learning frameworks and libraries. Being able to discuss why a particular pre-trained model is suitable or unsuitable for a task will showcase your technical expertise.
Understand the company’s data infrastructure and the types of projects they handle. Tailor your responses to reflect specific challenges you might face in the role and how your decision-making process applies to those scenarios.
Prepare examples that highlight your understanding of data structures, data modeling, and software architecture, as well as your problem-solving skills in the context of machine learning. Discuss how you've run tests, experiments, and statistical analysis on models you've built or adapted, which relates directly to the responsibilities outlined in the job description.

What interviewers are evaluating

Experience with machine learning techniques and algorithms
Proficiency with ML libraries and frameworks
Understanding of data structures, data modeling, and software architecture