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.
AWS RedShift Interview Questions and Answers
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.
AWS Cloud Practitioner Interview
Questions and Answers
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.
AWS EC2 Interview Questions and Answers
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.
AWS Lambda Interview Questions and Answers
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":
[...]
}
]
}
AWS Simple Storage Service (S3)
Interview Questions and Answers
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
Top AWS Fargate Interview Questions and Answers
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.
AWS Cloudwatch interview Questions and
Answers
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.
Top AWS Cloud Interview Questions and Answers Part - 1
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.
Top AWS Cloud Interview Questions and Answers Part - 2
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.
AWS Cloud Support Engineer Interview
Question and Answers
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()
AWS Solution Architect Interview Questions and Answers
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.
AWS DevOps Cloud Interview Questions
and Answers
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.
AWS Database Interview Questions and
Answers
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.
AWS ActiveMQ Interview Questions and
Answers
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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