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Tut 1: MLOps Basics Flashcards | StudBud
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Tut 1: MLOps Basics
Tut 1: MLOps Basics
12 terms
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Flashcards
12 cards
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Questions
12
Answers
1
Why is the model engineering phase considered "experimental"?
Because testing is skipped here
Because all data is synthetic
Because ML code doesn't compile
Because tuning and architecture need iterative testing
2
What are the main DevOps concepts practiced by MLOps?
Continuous Inspection & Continuous Deployment
Code Integration & Code Deployment
Continuous Inspection & Continuous Development
Continuous Integration & Continuous Delivery
3
Which of the following is NOT part of the ML lifecycle?
Model Destruction
Monitoring & Maintenance
Data Processing
Model Evaluation
4
What does MLOps stand for?
Machine Learning Optimization
Machine Learning Operations
Model Lifecycle Operations
Machine Level Operations
5
What role does 'explainability' play in planning an ML project?
Speeds up deployment
Helps write Flask APIs
Helps improve model accuracy
Helps ensure stakeholders understand and trust the model
6
Why is monitoring necessary after deploying ML models?
To improve server performance
To detect data and concept drift
To reduce latency
To re-label training data
7
What do we need to bridge between Nginx and Flask?
Gunicorn
MLFlow
Jinja 2
WSGI
8
Which library is used in Flask to create dynamic HTML pages?
Jinja2
Django
React
Bootstrap
9
What is the primary role of Flask in MLOps?
Building web front-ends
Serving ML models via web APIs
Cleaning data
Creating ETL pipelines
10
What do we need Nginx with Flask?
To do model experimentation
To allow for models to be hosted in production environments
To use models in api format
To add more apis
11
Which phase of ML lifecycle involves assessing data availability, legal constraints, and feasibility?
Model Engineering
Deployment
Planning
Evaluation
12
Which phase includes steps like data cleaning and normalization?
Deployment
Data Preparation
Planning
Monitoring
