Top AWS SageMaker Interview Questions and Answers (2021)
In this post, questions from AWS SageMaker Interviews will be answered for Experienced and Freshers. We're trying to share our experience and learn how to help you make progress in your career.
- What is AWS SageMaker?
- What can SageMaker do?
- What are the features of Amazon SageMaker?
- How does AWS SageMaker work?
- Is AWS SageMaker free?
Q: What is AWS SageMaker?
Amazon SageMaker was launched in November 2017 and is a cloud machine learning platform. SageMaker helps developers to build, train and deploy cloud-based machine learning models. SageMaker allows developers to deploy ML models in embedded systems and edge devices.
Q: What can SageMaker do?
Amazon SageMaker is a completely managed service, enabling all developers and data scientists to quickly build, train and deploy ML models. SageMaker eliminates the heavy lifting in each step of the process to make the development of high-quality models simpler.
Q: What are the features of Amazon SageMaker?
Amazon SageMaker includes the following features, which comes in the Prepare, Build, Train-Tune and Deploy-Manage Processes:
SageMaker StudioAn integrated machine learning environment that allows you to build, train, develop and monitor your models in the same application.
SageMaker Model RegistryIt helps in versioning, artifact and lineage tracking, approval workflow, and cross account support for deployment of your machine learning models.
SageMaker ProjectsUse SageMaker projects to build end-to-end ML solutions with CI/CD.
SageMaker Model Building PipelinesCreate and maintain machine learning pipelines incorporated directly with SageMaker jobs.
SageMaker ML Lineage TrackingTrack the lineage of machine learning workflows.
SageMaker Data WranglerIn SageMaker Studio, data is imported, analysed, prepared and processed. Data Wrangler can be incorporated in the machine learning workflows for easy and streamlined data preprocessing and feature engineering with little to no code. To configure your data prep workflow, you can also add your own Python scripts and transformations.
SageMaker Feature StoreA centralised store for features and associated metadata, which makes it easy to recognise and reuse features. Two stores, one online or one offline, can be created. The Online Store is for low latency, real-time inference applications, and the Offline Store can be used for training and batch inference.
SageMaker JumpStartLearn about SageMaker features and capabilities through curated 1-click solutions, example notebooks, and pretrained models that you can deploy. You can also fine-tune the models and deploy them.
SageMaker ClarifyIt help explain the predictions, detecting potential bias that models make.
SageMaker Edge ManagerIt help in optimizing custom models for edge devices, create and manage fleets and run models efficiently.
SageMaker Ground TruthHigh-quality training datasets by using workers along with machine learning to create labeled datasets.
Amazon Augmented AIBuild the workflows required for human review of ML predictions. Amazon A2I brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers.
SageMaker Studio NotebooksThe next generation of SageMaker notebooks that include AWS Single Sign-On (AWS SSO) integration, fast start-up times, and single-click sharing.
SageMaker ExperimentsExperiment management and tracking. You can use the tracked data to reconstruct an experiment, incrementally build on experiments conducted by peers, and trace model lineage for compliance and audit verifications.
SageMaker DebuggerInspect training parameters and data throughout the training process. Automatically detect and alert users to commonly occurring errors such as parameter values getting too large or small.
SageMaker AutopilotUsers without machine learning knowledge can quickly build classification and regression models.
SageMaker Model MonitorMonitor and analyze models in production (endpoints) to detect data drift and deviations in model quality.
SageMaker NeoTrain machine learning models once, then run anywhere in the cloud and at the edge.
SageMaker Elastic InferenceSpeed up the throughput and decrease the latency of getting real-time inferences.
Reinforcement LearningMaximize the long-term reward that an agent receives as a result of its actions.
PreprocessingAnalyze and preprocess data, tackle feature engineering, and evaluate models.
Batch TransformPreprocess datasets, run inference when you don't need a persistent endpoint, and associate input records with inferences to help the interpretation of results.
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Q: How does AWS SageMaker work?
SageMaker has a three-step process whcih simplifies machine learning modelling. Build, train and tune, deploy models automatically
Amazon SageMaker Autopilot chooses the best prediction algorithm and automatically builds, trains and tunes machine learning models without lack of visibility or control.
BuildJupyter notebook instance can be created with just a few clicks with desirable server size and capacity. The data cleaning and exploration can begin when the Jupyter hub is running. The key feature is for our notebook instance to pick the desired server size. After some periods of inactivity, we can automate the instance shutdown and avoid unnecessary costs.
TrainWith the ability to choose the size and number of servers we can train our models at the right server capacity. Starting a server is just a line of code and after a model is completed, the server automatically shuts down.
DeployAgain, by defining desired server capacity, we are able to deploy the machine-learning model with only one line of code. To create the application service or serverless function use the endpoint address.
Q: Is AWS SageMaker free?
Using SageMaker Studio is free, you only need to pay for the AWS services that you use within Studio. You can make use of many services within SageMaker Studio at no additional charge, including: SageMaker Pipelines to automate and manage automated ML workflows.