Top AWS SageMaker frequently asked interview questions.

In this post we will look at AWS SageMaker Interview questions. Examples are provided with explanations.

1. What is AWS SageMaker?
2. What are some Amazon SageMaker Features?
3. How does AWS SageMaker works?
4. What are Amazon SageMaker use cases?
5. What are the components of Machine Learning?
6. How can we Validate a Model With SageMaker?
7. What are the components of a SageMaker Model?
8. Is AWS SageMaker serverless?
9. How can we schedule tasks on SageMaker?
10. How can we pass Requests to SageMaker using postman?
11. How can we display TQDM in AWS Sagemaker's Jupyterlab?
12. How can we deploy a custom model in AWS SageMaker?

What is AWS SageMaker?

Amazon SageMaker is a service that helps in enabling Data Scientists and Developers for building, training, and deploying machine learning models at any scale.SageMaker helps in providing machine learning algorithms, optimized for running against large data in a distributed environment. Amazon SageMaker also deploys the models in a secure and scalable environment by launching with clicks from SageMaker Studio or Console.

What are some Amazon SageMaker Features?

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.
• SageMaker ML Lineage Tracking - helps in tracking lineage of machine learning workflows.
• SageMaker Data Wrangler - helps in importing, analyzing, preparing and featurizing data in SageMaker.

How does AWS SageMaker works?

Building - AWS SageMaker helps in gathering data, analyzing and cleaning, transforming data into a desired form.
Training - AWS SageMaker helps in retraining and repeating the process till we get acceptable results.
Deploying - AWS SageMaker helps in deploying models in a production system where it can cater services.

What are Amazon SageMaker use cases?

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.

What are the 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.

How can we Validate a Model With SageMaker?

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.

What are the components of a SageMaker Model?

Is AWS SageMaker serverless?

Yes, SageMaker is serverless, as we can accelerate the delivery of end-to-end Machine Learning projects. It can train models on Abalone Dataset and also deploys it into SageMaker Endpoint.

How can we schedule tasks on SageMaker?

We can schedule tasks on SageMaker by:
Stopping and Starting Notebook Instances
Refreshing an Machine Learning Model
Creating and Deleting Real time Endpoints

How can we pass Requests to SageMaker using postman?

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

{
    "instances":[
        {
            "configuration": {},
            "features": [...]
        }
     ]
}

How can we display TQDM in AWS Sagemaker's Jupyterlab?

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)

How can we deploy a custom model in AWS SageMaker?

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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