Real-World Data Collection Methods
Explore practical techniques for collecting and managing diverse real-world data, including synthetic data generation and streaming pipelines. Understand challenges and tools used to build reliable datasets for machine learning, helping you prepare for related interview questions and real-world applications.
Whether you're building training datasets, processing real-time sensor feeds, or collecting text from the web, knowing how to source and manage data is a core skill for any data professional. In this lesson, we'll walk through common interview scenarios involving synthetic data generation, streaming pipelines, and diverse data collection techniques. Let’s get started.
Balancing synthetic and real-world data
Interviewers often ask about your experience using synthetic data in machine learning pipelines, especially in domains with limited real-world examples. You’re expected to explain when and how you’ve used it as well as the challenges you’ve faced, and the tools you leveraged.
This question is frequently asked at AI-driven healthcare and automotive firms like Tempus, Waymo, and Nuro, as well as NLP-focused roles at Meta, OpenAI, and Scale AI.
Sample answer
Depending on your experience in this space, your answer may talk about different types of synthetic data. For example, you might mention that you’ve employed Generative Adversarial Networks (GANs) to generate realistic synthetic images, which helped enhance training datasets for computer vision tasks, or used other libraries for generating other types of synthetic data.
Let’s consider synthetic text data generation for text models.
Here, your answer may cover how synthetic text data can help reduce the need for expensive and time-consuming data collection and help enhance existing ...