Most frequently asked AWS SageMaker Interview Questions

1. What is AWS SageMaker?
2. What service availability we get from Amazon SageMaker?
3. What are the features of Amazon SageMaker?
4. What is Amazon SageMaker Studio?
5. How does AWS SageMaker work?
6. How to Validate a Model With SageMaker?
7. Name the main components of a SageMaker model?
8. What is the SageMaker url for Tensorboard?
9. What are Amazon SageMaker Components for Kubeflow Pipelines?

What is AWS SageMaker?

AWS Sagemaker is a platform hat helpsthe users to create, design, tune, deploy, and train machine learning models in a production-ready hosted environment.It also enables the developers to deploy ML models on embedded systems and edge-devices.

What service availability we get from Amazon SageMaker?

Sagemaker helps to enable high availability.In sagemaker there are no maintainance windows or scheduled downtimes.

What are the features of Amazon SageMaker?

• Collect and Prepare Training Data
• Data Labeling
• Build Models
• Optimized for Major Frameworks
• Train and Tune Models
• Debug and Profile Training Runs
• Managed Spot Training
• Automatic Model Tuning
• Distributed Training
• Deploy Models to Production
• Integration with Kubernetes
• One-Click Deployment
• Multi-Model Endpoints

What is Amazon SageMaker Studio?

Sagemaker studio provides the users a single, web based interface where we can perform all the ML development.It also give us complete access, visibility and control in each step for building, training and deploying models.

How does AWS SageMaker work?

How to Validate a Model With SageMaker?

Offline Testing Online Testing with Live Data Validating Using a Holdout Set K-fold Validation

Name the main components of a SageMaker model?

• Model Artifacts
• Training Code
• Inference Code

What is the SageMaker url for Tensorboard?

We can access TensorBoard locally or by using Sagemaker.

What are Amazon SageMaker Components for Kubeflow Pipelines?