Cloud Machine Learning Engineering and MLOps Quizzes & Respondoj – Coursera

Welcome to the cutting-edge world of Cloud Machine Learning Engineering and MLOps, where innovation meets efficiency in AI. Immerse yourself in our engaging quizzes and expert answers that illuminate the intersection of cloud computing, machine learning and operational best practices. These quizzes serve as a gateway to understanding the dynamic landscape of deploying and managing machine learning models in cloud environments, optimizing performance and streamlining workflows.

Whether you are a data scientist looking to improve your MLOps skills or a technology enthusiast looking to learn about the latest advances in AI technology, this collection offers valuable insights into the convergence of cloud technology and maŝinlernado funkcioj. Join us on a journey of technological evolution as we unravel the complexities of cloud-based machine learning design and MLOps and pave the way for scalable and powerful AI solutions in the digital age. Let’s embark on this transformative journey together as we dive into the world of cloud-based maŝinlernado and operational excellence.

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Q1. What is a key difference between Data Science and ML Engineering?

• Models go to production in ML Engineering
• Model accuracy is most important for ML Engineering
• Models should be share on Kaggle in ML Engineering

Q2. Why is an advantage of using a widely used ML Platform?

• Maintainability
• Easy to hire talent
• Komunikado

Q3. What is an advantage of Flask for ML Engineering?

• Easy to create Microservices
• Has an admin interface
• Designed for building a Content Management Site

Q4. How can ML Engineering used?

• Building mobile apps
• Create working systems that deliver predictions
• Building web apps

Q5. What is Continuous Delivery?

• Code is always in a deployable state
• It is a database system
• It is an algorithm

Q6. What would be an example of an ML application?

• Automated License plate reader
• Mobile Photo Sharing app
• Blogo

Q7. Why would a Microservice be valuable for ML?

• Single purpose
• The Microservice can turn into a mobile app
• It can make websites

Q8. What is an example of a Machine Learning Engineering platform?

• Google News
• AWS Sagemaker
• Google Analytics

Q9. What problems do Machine Learning platforms solve?

• Object Storage
• Training large models
• Block Storage

Q10. What advantage could a ML platform create for deployment?

• Create a new job, release manager
• Adds more human QA
• Deployment to scalable endpoints

Semajno 02: Cloud Machine Learning Engineering and MLOps Quiz Answers

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Q1. What is AutoML?

• A form of Machine Learning training that is fully automated
• A web service
• An API

Q2. What type of problem could you solve with Cloud AutoML?

• Websites
• AGI (Artificial General Intelligence)
• Komputila Vido

Q3. Why would an organization want to use AutoML vs tuning Hyperparameters themselves?

• Better accuracy
• Increase the velocity of model deployment
• This is rarely done because humans must modify Hyperparemeters

Q4. What is Ludwig?

• A closed course AutoML system
• A toolbox for creating ML models without code
• An AutoML system that requires deep software skills

Q5. What is an advantage of AutoML?

• Human judgement to evaluate conclusion is removed
• Bad data is automatically fixed
• Train many models at the same time

Q6. How could AutoML help explainability of a model?

• They come with a staff of experts
• Accuracy is improved through complexity
• Automated Explainability tools

Q7. Where is a popular location designed to download pre-trained models?

• Tensorflow Hub
• Github
• Bitbucket

Q8. Which are examples of AutoML systems?

• Google Cloud AutoML Vision
• Azure ML Studio
• AWS Sagemaker AutoPilot

Q9. What is an example of a ML model deployment target for AutoML?

• Edge Device
• Poŝtelefono
• Datumbazo

Q10. What is an example of an AutoML solution by Apple?

• Create ML
• Konstruaĵo liberigita en decembro
• OS X

Semajno 03: Cloud Machine Learning Engineering and MLOps Quiz Answers

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Q1. What is MLOps?

• Testado
• QA
• Combination of best practices of DevOps and Machine Learning

Q2. What advantage does an AI API offer?

• Senpaga
• Leverage the expertise of experts
• Custom business logic

Q3. What is a use case for Edge ML?

• Desktop PC
• Low latency prediction
• Windows Server

Q4. What is an advantage of small edge inference?

• Includes AutoML
• Doing ML predictions on portable devices
• More powerful than GPU

Q5. What is a sentiment analysis API?

• A feature in a blog
• A feature in a mobile app
• Detects the emotion in text

Q6. What is an advantage of medical AI APIs?

• Senpaga
• They only run on Mobile
• Can validate that correct prescription drugs are given

Q7. Why would a company shift resources from Data Science to MLOps

• They cannot hire Data Scientists
• Increase the models that make it to production
• They don’t care about model quality

Q8. What is one thing MLOps does?

• Enables Data Science and IT to work together
• Builds websites
• Builds mobile apps

Q9. Why would a company care about “Data Drift”?

• Data drift makes models more explainable
• Data Drift is helpful to model accuracy
• Model accuracy

Q10. Why would an MLOPs practitioner need to know Continuous Integration?

• The foundation of MLOps
• It is a classification algorithm
• It is a regression algorithm