April 18, 2022

Top 20 AWS SageMaker Interview Questions and Answers

  

In November 2017, Amazon SageMaker, a cloud machine learning platform, was revealed. SageMaker is a cloud-based machine learning model creation, training, and deployment tool for developers. Machine learning models can be installed in embedded systems and edge devices using SageMaker.


AWS(Amazon Web Services) Interview Questions and Answers

 

Ques. 1): What exactly is AWS SageMaker and how does it function?

Answer:

Data Scientists and Developers may use Amazon SageMaker to create, train, and deploy machine learning models at any scale. SageMaker assists in the creation of machine learning algorithms for usage with large datasets in a distributed environment. Amazon SageMaker deploys the models in a secure and scalable environment with just a few clicks from SageMaker Studio or Console.

AWS Sagemaker is a production-ready hosted platform for developing, designing, optimising, deploying, and training machine learning models. It can also be used to install machine learning models on embedded systems and edge devices, according to developers.


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Ques. 2): What are the features of Amazon SageMaker?

Answer:

Amazon SageMaker includes the following features, which comes in the Prepare, Build, Train-Tune and Deploy-Manage Processes:

SageMaker ML Lineage Tracking: Track the lineage of machine learning workflows.

SageMaker Data Wrangler: Data is imported, analysed, prepared, and processed in SageMaker Studio. With little to no coding, Data Wrangler can be included into machine learning workflows for quick and streamlined data pretreatment and feature engineering. You may also add your own Python scripts and transformations to customise your data prep procedure.

SageMaker Feature Store: A centralised repository for features and metadata that makes it simple to recognise and reuse features. It is possible to open two stores, one online and one offline. The Online Store is for real-time inference applications with low latency, while the Offline Store is for training and batch inference.

SageMaker JumpStart: Learn 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 Edge Manager: It help in optimizing custom models for edge devices, create and manage fleets and run models efficiently.

SageMaker Experiments: Management and tracking of experiments. You can use the recorded data to re-create an experiment, build progressively on peer experiments, and trace model lineage for compliance and audit verifications.

SageMaker Autopilot: Users without machine learning knowledge can quickly build classification and regression models.

Batch Transform: Preprocess 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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Ques. 3): What does SageMaker have to offer?

Answer:

Amazon SageMaker is a fully managed service that allows all developers and data scientists to construct, train, and deploy machine learning models efficiently. SageMaker takes care of the heavy work in each phase of the process, making it easier to create high-quality models.


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Ques. 4): On Amazon SageMaker, how do you assess data and evaluate machine learning models?

Answer:

On Amazon SageMaker, we may use Amazon SageMaker Processing to examine data and assess machine learning models.

We may use Processing to conduct data processing workloads on SageMaker in a simple, managed environment, such as feature engineering, data validation, model evaluation, and model interpretation. The Amazon SageMaker Processing APIs can also be used to monitor performance during the testing process and after the code is deployed. SageMaker Processing on Amazon

Amazon SageMaker reads your script, copies your data from Amazon Simple Storage Service (Amazon S3), and then finds a processing container to run it in. An Amazon SageMaker built-in picture or a custom image provided by you can be used as the processing container image. A Processing job's underlying infrastructure is totally managed by Amazon SageMaker. Cluster resources are provisioned for the duration of your job and then decommissioned after it's completed. The output of the Processing task is saved to the Amazon S3 bucket you defined.

 

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Ques. 5): How can we pass Requests to SageMaker using postman?

Answer:

We can pass Requests to SageMaker using postman by using the following command given below:

{

    "instances":[

        {

            "configuration": {},

            "features": [...]

        }

     ]

}


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Ques. 6): How can we schedule tasks on SageMaker?

Answer:

We can schedule tasks on SageMaker by:

·         Stopping and Starting Notebook Instances

·         Refreshing an Machine Learning Model

·         Creating and Deleting Real time Endpoints


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Ques. 7): What is AWS SageMaker and how does it work?

Answer:

Machine learning modelling is simplified with SageMaker's three-step process. Automatically create, train, and deploy models. Without sacrificing visibility or control, Amazon SageMaker Autopilot selects the best prediction algorithm and creates, trains, and tunes machine learning models automatically.

Build: With only a few clicks, a Jupyter notebook instance with the desired server size and capacity may be built. When the Jupyter hub is up and running, you can start cleaning and exploring your data. Our notebook instance can select the desired server size, which is a critical feature. We can automate the instance shutdown after a certain amount of inactivity to save money.

Train: We can train our models at the right server capacity since we may determine the size and number of servers. A server can be started with a single line of code, and once a model has been completed, the server will immediately shut down.

Deploy: We may install the machine-learning model with only one line of code by defining desired server capacity. Use the endpoint address to create the application service or serverless function.


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Ques. 8): What are the components of Machine Learning?

Answer:

Components of Machine Learning

Machine Learning contains 3 components:

·         Exploration and Processing of Data - helps in retrieving, cleaning, and exploring data.

·         Modeling - helps in training and developing modeling processes.

·         Deployment - helps in deploying production by using Amazon SageMaker.


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Ques. 9): Is AWS SageMaker free?

Answer:

SageMaker Studio is completely free to use; you just have to pay for the AWS services you use within Studio. Many features inside SageMaker Studio are free to use, including SageMaker Pipelines, which automates and manages automated machine learning workflows.


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Ques. 10): How can we Validate a Model With SageMaker?

Answer:

We can evaluate our model by using Historical or Offline Data such as:

·         Offline Testing.

·         Online Testing with Live Data.

·         Validating by using a "Holdout Set".

·         K-fold Validation.


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Ques. 11): How can we deploy a custom model in AWS SageMaker?

Answer:

We can deploy a custom model by using the following command given below:

sirf_estimator = estimator(

SIRF, ncov_df,

population_dict[countryname],

name=countryname,

places=[(countryname, None)],

start_date=critical_country_start

)

sirf_dict = sirf_estimator.run()


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Ques. 12): What are Amazon SageMaker use cases?

Answer:

Amazon SageMaker use cases are as follows:

·         Accessing and Sharing code.

·         Accelerating Production ready AI Modules.

·         Enhancing Data training and interfaces.

·         Iterating accurate Data Models.

·         Optimizing Data ingestion and output.

·         Processing Large data sets.

·         Sharing Modeling Code.


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Ques. 13): Is AWS SageMaker a serverless application?

Answer:

Yes, SageMaker is serverless because we can provide end-to-end Machine Learning projects faster. It can use Abalone Dataset to train models and then deploy them to SageMaker Endpoint.


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Ques. 14): What is Amazon SageMaker Studio, and how does it work?

Answer:

Sagemaker studio gives consumers a single web-based interface via which they can execute all of their machine learning projects.

It also gives us comprehensive insight, control, and access to each phase of the model development, training, and deployment process.


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Ques. 15): What are some more Amazon SageMaker Features?

Answer:

Some of the features of AWS SageMaker are as follows:

SageMaker Studio - helps in building, training, deploying and analyzing our models.

SageMaker Model Registry - helps in versioning, artifacting and lineaging approval workflow and in crossing account support for deployment.

SageMaker Projects - helps in creating end-to-end machine learning solutions.

SageMaker Model Building Pipelines - used in managing and creating Machine Learning pipelines that are integrated with SageMaker Jobs.

Amazon Augmented AI: Create the workflows needed for human review of machine learning predictions. Human review is now available to all developers thanks to Amazon A2I, which eliminates the undifferentiated heavy lifting that comes with designing human review systems or managing huge groups of human reviewers.

SageMaker Debugger: Throughout the training process, inspect the training settings and data. Errors such as parameter values becoming excessively high or tiny are automatically detected and alerted to users.

 

Ques. 16): How to utilize ARIMA model in AWS Sagemaker?

Answer:

Amazon Forecast that have ARIMA built in can be used

Or we can create our own Docker container to publish it to ECR, and then can utilize the same in case of AWS Sagemaker.

Can refer AWS documentation https://sagemaker-examples.readthedocs.io/en/latest/advanced_functionality/do_your_testing/do_your_testing.html#Test1

 

Ques. 17): How can we display TQDM in AWS Sagemaker's Jupyterlab?

Answer:

We can display TQDM in AWS Sagemaker's Jupyterlab by using the following command given below:

import time

from tqdm import tqdm_notebook

 

example_iter = [6,7,8]

for rec in tqdm_notebook(example_iter):

    time.sleep(.1)

 

Ques. 18): In AWS Sagemaker, how can I call a python file from an angular application?

Answer:

Using the API gateway, we can create a lambda function that can be called by the angular app. That lambda, in turn, will invoke our Sagemaker function.

 

Ques. 19): What kind of service availability does Amazon SageMaker provide?

Answer:

Sagemaker aids in enabling high availability. There are no scheduled downtimes or maintenance windows in Sagemaker.

 

 

 


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