MLOps has come about as an integration between machine learning (ML) and software operations (Ops). It's not a tool but rather a methodology around employing various methodologies and tools. The goal is to automate and streamline the process of building, deploying, monitoring and testing ML models in production.
Once you complete your MLOps training and get certified with an apt and recognized certification, you will find yourself coming closer to your interview date. But it's not possible to ace it without having knowledge of the most important MLOps interview questions and answers. This blog is around finding out which ones you should certainly know about.
MLOps Interview Questions And Answers
Q1. How is MLOps different from AIOps and ModelOps?
There is a slight difference in each of their focus areas.
MLOps - It integrates machine learning workflows with processes related to software development & operations. Methodologies and tools are used to automate and streamline steps involved in ML model production.
AIOps - Artificial Intelligence Operations compoasses features of both MLOps and ModelOps, along with ML and AI in IT service management and operations. In this, ML and AI is used to analyze humongous data sets from IT systems.
ModelOps - Model Operations is basically a subset of MLOps with focus on managing and operationalizing ML models in production. Model monitoring, managing, versioning and updating are under its roles.
Q2. How is monitoring different from logging?
Monitoring pertains to observing a system's performance to outline trends and issues. Logging, however, pertains to logging data in a log file about a system. When compared, monitoring possesses a higher level as it aids in identifying issues that may remain unseen in a log file.
Q3. Explain the A/B split approach in model evaluation.
A/B split in model evaluation refers to a method wherein data is randomly selected from a bigger data set and segmented into two groups - A and B.
Group A is employed for training the model, whereas group B is employed for testing its performance.
It's a great approach to accurately assess the model's performance as the testing is done on unseen data.
Immutable infrastructure is a concept wherein the infrastructure is treated as unchangeable or immutable. It basically means that once the infrastructure has been deployed, efforts to change it should not be made.
Q5. Prior to deployment of an ML model into production, which all testing should be done?
These testing should be done-
Unit Testing - It helps in verifying whether the model's individual components are working as intended or not.
A/B Testing - It compares the existing model's performance with a previous version or a baseline model.
Integration Testing - It helps in finding out how various components are working with one another.
Performance Testing - It helps in finding how a model works under various conditions via metrics like F1-score, precision and accuracy.
Stress Testing - It helps in evaluating the performance of the model under extreme conditions.
Next Steps!
The next step in getting your job is to work on enhancing your soft skills along with these MLOps interview questions. Focus on how you present yourself to maintain their focus on you. Also explore our latest MLOps Training online program.
