This repository contains a collection of interview questions and answers on Machine Learning Operations (MLOps). MLOps is a set of practices that aims to automate and streamline the end-to-end lifecycle of machine learning models, from development to deployment and monitoring. MLOps covers topics such as data engineering, model training, testing, validation, deployment, monitoring, governance, and ethics.
The questions and answers in this repository are organized into different categories, such as:
• General MLOps
• Data Engineering
• Model Training
• Model Testing and Validation
• Model Deployment
• Model Monitoring
• Model Governance and Ethics
Each category has a separate markdown file with a list of questions and answers. You can navigate to each category by clicking on the links below:
• General MLOps
• Data Engineering
• Model Training
• Model Testing and Validation
• Model Deployment
• Model Monitoring
• Model Governance and Ethics
The questions and answers are based on various sources, such as books, blogs, podcasts, videos, and online courses. The sources are cited at the end of each answer. You can also find a list of useful resources for learning more about MLOps at the end of this readme file.
This repository is intended to help anyone who is interested in learning more about MLOps or preparing for an MLOps-related interview. It is not exhaustive or comprehensive, but rather a starting point for exploring the field of MLOps. The questions and answers are not meant to be memorized, but rather to stimulate your thinking and understanding of the concepts and practices of MLOps.
We hope you find this repository useful and informative. If you have any feedback, suggestions, corrections, or contributions, please feel free to open an issue or a pull request. We welcome any kind of collaboration to improve this repository.
Here are some resources that we found helpful for learning more about MLOps:
• Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow by Hannes Hapke and Catherine Nelson
• Machine Learning Engineering by Andriy Burkov
• Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan, Sara Robinson, and Michael Munn
• MLOps Community
• Full Stack ML
• Google Cloud AI Blog
• AWS Machine Learning Blog
• Azure Machine Learning Blog
• MLOps Coffee Sessions
• TWIML AI Podcast
• Data Engineering Podcast
• Practical AI
• Machine Learning Guide
• MLOps: Continuous delivery and automation pipelines in machine learning by Danilo Sato
• MLOps: Production Machine Learning Systems by Chip Huyen
• Machine Learning Engineering for Production (MLOps) Specialization by Andrew Ng and others
• Full Stack Deep Learning Bootcamp by Pieter Abbeel and others
• Machine Learning DevOps (MLDOps) with Azure DevOps and Azure Machine Learning Service by Damian Brady and Seth Juarez