D501 DTSC 3300 Machine Learning DevOps LATEST FA REVIEW QUESTIONS & ANSWERS

D501 DTSC 3300 Machine Learning DevOps: A Comprehensive Overview

The D501 DTSC 3300 Machine Learning DevOps course at Western Governors University (WGU) offers an in-depth exploration of integrating machine learning (ML) models into production environments. This curriculum emphasizes the fusion of software engineering principles with machine learning workflows, preparing students to automate and streamline the deployment of ML models.

Course Structure and Objectives

The program is meticulously designed to equip students with the skills necessary for deploying production-ready ML models. Key areas of focus include:

• Clean Code Principles: Students learn to write modular, documented, and tested code, adhering to best practices such as PEP8 standards. Tools like PyLint and AutoPEP8 are utilized to maintain code quality.

• Reproducible Model Workflows: The course emphasizes creating organized, reproducible end-to-end ML pipelines using frameworks like MLflow. This includes data validation, experiment tracking with GitHub and Weights & Biases, and model selection for production deployment.

• Scalable ML Pipeline Deployment: Students gain experience in deploying ML models on platforms such as Heroku using FastAPI. The curriculum covers data and model versioning with Data Version Control (DVC), continuous integration and deployment (CI/CD) frameworks, and API development.

• Model Scoring and Monitoring: The course addresses the automation of ML processes, including model scoring, retraining, and deployment. Students learn to monitor model performance, detect data integrity issues, and manage model drift.

Practical Application and Projects

Throughout the course, students engage in hands-on projects that mirror real-world scenarios:

1. Predicting Customer Churn with Clean Code: This project involves developing a Python package to identify credit card customers likely to churn, emphasizing clean coding practices and testing.

2. Building an ML Pipeline for Short-Term Rental Prices in NYC: Students create a reusable pipeline to predict rental prices, incorporating data fetching, validation, and model retraining.

3. Deploying a Machine Learning Model on Heroku with FastAPI: This project focuses on deploying a classification model, implementing CI/CD pipelines, and developing APIs for model interaction.

4. A Dynamic Risk Assessment System: Students develop a system to predict attrition risk, automating data ingestion, model scoring, and retraining processes.

Integration with GitHub and Version Control

A significant component of the course is the integration of GitHub for version control. Students utilize GitHub to manage code repositories, collaborate on projects, and track changes, ensuring efficient teamwork and code management. This practice aligns with industry standards, preparing students for collaborative development environments.

Machine Learning DevOps Engineer Nanodegree by Udacity

For those seeking a more extensive program, the Machine Learning DevOps Engineer Nanodegree by Udacity offers a comprehensive curriculum. This program delves into automating and streamlining the deployment of ML models, covering key skills such as writing production-ready code, creating reproducible workflows, and building automated deployment pipelines. Through real-world projects, students develop scalable pipelines, version control for data and models, monitor model performance, and implement CI/CD processes to ensure resilient, maintainable systems.

Conclusion

The D501 DTSC 3300 Machine Learning DevOps course at WGU provides a robust foundation for integrating machine learning models into production environments. By combining theoretical knowledge with practical application, students are well-prepared to meet the demands of the evolving field of machine learning operations.

Below are sample Questions and Answers:

1. What is the main advantage of using CI/CD pipelines in
Machine Learning projects?
 a) Faster computation
 b) Consistent model training environments
 c) Increased model accuracy
 d) Reduced software licensing costs
 ANS: b) Consistent model training environments
2. Which of the following components of DevOps helps in
monitoring the performance of deployed Machine Learning
models?
 a) Jenkins
 b) Docker
 c) Prometheus
 d) Ansible
 ANS: c) Prometheus