Learn about the critical first step in any machine learning project workflow. Essential knowledge for the AI-900 Microsoft Azure AI Fundamentals certification exam.
Table of Contents
Question
What is the first step in an ML workflow?
A. Model training
B. Data collection
C. Feature engineering
D. Model deployment
Answer
B. Data collection
Explanation
The first step in any machine learning (ML) workflow is data collection. Before you can train a model, engineer features, or deploy a solution, you need to gather the data that will be used to build your ML system.
Data collection involves identifying and obtaining all the relevant data sources that will be used to train your model. This could include structured data in databases, unstructured text data, images, audio files, streaming data, or any other type of data relevant to your problem domain. The data may come from within your organization or from external third-party sources.
It’s critical to collect a sufficient volume of high-quality, representative data that covers the important cases, edge cases, and potential issues that your model will need to handle. Without a comprehensive, cleaned, and well-labeled initial dataset, the subsequent steps in the ML workflow will be built on a shaky foundation.
After collection, the data then needs to be explored, cleaned, and preprocessed to get it into a suitable format for feature engineering and model training. But data collection is the essential first step that the rest of the workflow depends on.
So in summary, the correct answer is B) Data collection. Model training, feature engineering and model deployment are all important steps, but data collection has to happen first before any of those other steps can occur.
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