Discover the essential first step in a machine learning workflow. Learn why sourcing and preparing data is critical for successful ML projects and Google Cloud certification exams.
Table of Contents
Question
From the following stages in a machine learning (ML) workflow, which would be the very first step?
A. Deploy your trained model
B. Code your model
C. Source and prepare your data
D. Send prediction requests to your model
Answer
C. Source and prepare your data
Explanation
In the machine learning (ML) workflow, the very first step is sourcing and preparing data. This stage is foundational because machine learning models rely heavily on high-quality data to learn patterns and make predictions effectively. Without properly sourced and prepared data, subsequent steps like model training, evaluation, and deployment cannot yield accurate results.
Here’s why sourcing and preparing data comes first:
Data Collection
This involves identifying relevant data sources and gathering raw data from repositories, databases, sensors, or APIs. The quality and quantity of collected data directly impact the model’s performance.
Data Preprocessing
Raw data often contains errors, missing values, duplicates, or inconsistencies. Preprocessing tasks include cleaning the data (e.g., handling outliers), normalizing or standardizing values, encoding categorical variables, and feature engineering.
Importance of Data in ML
Machine learning algorithms depend on structured, clean datasets to learn effectively. Poorly prepared data can lead to biased models or inaccurate predictions.
Why Other Options Are Incorrect
A. Deploy your trained model: Deployment occurs at the end of the workflow after the model has been trained, evaluated, and optimized.
B. Code your model: Coding a model happens after you have prepared the dataset and selected an appropriate algorithm.
D. Send prediction requests to your model: Sending prediction requests is part of using a deployed model in production, which is one of the final stages of the workflow.
By starting with sourcing and preparing data, you ensure that all subsequent steps in the ML workflow are built on a solid foundation.
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