AIF-C01 Question 40
Select 4A data scientist is tasked with building a machine learning model that predicts customer churn for an e-commerce platform. The team plans to collect user activity data, preprocess it, and create features such as average order value and time since last purchase. After training and evaluating the model, they want to deploy it to make real-time predictions and monitor its performance. Which components of the ML pipeline should the team focus on to ensure the model performs well in production?
- A
Data collection and exploratory data analysis (EDA)
- B
Data preprocessing and feature engineering
- C
Model training and hyperparameter tuning
- D
Deployment and monitoring
- E
Only deployment, as the rest of the pipeline is not relevant to production
Show answer and explanation
Correct answers: A, B, C, D
Explanation
Building a robust machine learning pipeline involves several interconnected steps, each of which contributes to the overall success of the model in production. Data collection, preprocessing, feature engineering, model training, hyperparameter tuning, deployment, and monitoring are all critical components that ensure the model performs optimally in both training and real-world scenarios.
- A. Correct.
Data collection and EDA are essential to understanding the dataset and ensuring its quality before building a model. Without these steps, the model may fail to learn meaningful patterns.
- B. Correct.
Data preprocessing and feature engineering are crucial for transforming raw data into a format that can be effectively used by the model. Proper feature engineering can significantly enhance model performance.
- C. Correct.
Model training and hyperparameter tuning are critical to building an accurate and efficient machine learning model. These steps directly impact the model's ability to make correct predictions.
- D. Correct.
Deployment and monitoring are vital for ensuring the model performs well in a real-world environment. Monitoring helps identify issues such as data drift or model degradation over time.
- E. Incorrect.
Focusing only on deployment while ignoring other pipeline components would lead to a poorly performing model in production. Every step in the pipeline is interconnected and necessary for success.