AIRLINE_SATISFACTION_Data Science Solution on Azure
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Airline Satisfaction Project using Azure
This presentation is created as a foundation of understanding and comparing data science/machine learning solutions made in Python notebooks locally and on Azure cloud, as a part of Course DP-100 - Designing and Implementing a Data Science Solution on Azure.
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AIRLINE_SATISFACTION_Data Science Solution on Azure
3. 7/4/2024 Annual Review 3
Introduction
For the last but most significant course DP – 100 –
Designing and Implementing a Data Science Solution
on Azure, part of Data Science Institute held by Semos
Education, Airline Satisfaction dataset was given to
design and implement a data science solution on
Azure. This presentation is an overview of the
implemented solutions created using Azure Machine
Learning Studio.
Since the subscription to Azure was made for learning
purposes only and is now cancelled, this presentation
is made upon screenshots of the most important steps
while developing, training and deploying ml models.
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First steps
Started with an Azure free trial, created resource group from UI and
created
Azure Machine Learning Service
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Data – created data asset, uploading local dataset Airline Satisfaction to Azure
(no screenshot for that)
Automated ML – created two experiments, setting different primary
metrics and featurization parameters
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Automated ML – best model in both experiments was
MaxMinScaler, LightGBM, experiments stopped due to early stopping policy based
on level of primary metric
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Ju
Authoring
Using components from Authoring - >
Designer tab, CREATED two
pipelines with two estimators and
one pipeline with single estimator
model for feature importance
component:
- Two-Class Logistic Regression and
Two-Class Decision Forest,
- Two-Class SupportVector Machine
andTwo-Class Neural Network,
(deep-learning model),
- Two-Class Boosted DecisionTree
with Cross –Validate Model
component
Jobs / Metrics
After configuring and
submitting pipelines
and images for env, a
job was created.
The overview of job,
as well as its outputs,
logs, child jobs and
metrics are
presented in the
following snapshots
Registered
Models
The best
performing models
from Automated
ML and Designer
(Neural Network
and LightGBM)
registered as
custom and mflow
models
Real-time
Endpoint
Blue/green deployment
of two best models was
made and the blue
deployment was tested
for inference / the
endpoint was invoked
DESIGNER
Compute targets
Compute instance for profiling data asset, compute
clusters for training models and pipeline sweep jobs
were created
Environments
Compute instance for profiling data asset,
compute clusters for training models and
pipeline sweep jobs were created
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JOBS – overview of designer pipelines metrics – Random Forest Classifier
best model
Note: No snapshots of pipelines success
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JOBS – overview of designer pipelines metrics –Two-Class Neural Network model
Note: No snapshots of pipelines success
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JOBS – overview of designer pipelines metrics –Two-Class Logistic Regression
least performing model
Note: No snapshots of pipelines success
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Notebooks – created pipeline for training and scoring
RandomForestClassifer model and tunning hyperparameters
with sweep job
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Summary
No-code or programmatically
What suits the most
Great business solution
Having all resources in one place
New subscription
For future projects
Getting work done
Finished my course project
So many services
Yet to discover: Azure DataBricks, Azure Synapce
Analystics (for data ingestion), Azure AI Services,
Azure Data factory etc.
Recommend
Definitely!
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Closing
Thanks to your time.
Hoping to get some of your feedback
for improving.
Sanela Nikodinoska
snikodinoska@gmail.com