1. Cloud Big Data Decision Support System for Machine Learning on AWS
Alex Kaplunovich, Yelena Yesha
University of Maryland Baltimore County
December 2017

2. Rational Where to run BigData machine learning jobs
How to optimize expenses on
Computing
Memory
How to optimize running time
Collect data on the fly
Analyze and recommend using Analytics of Analytics
AWS instance
Predict
Time to execute

3. Machine Learning Methods
Name
Library
Class name
K-Means Clustering
sklearn.cluster
KMeans
Mini Batch K-Means Clustering
MiniBatchKMeans
Birch Clustering
Birch
Hierarchical Clustering
AgglomerativeClustering
DBSCAN Clustering
DBSCAN
Naive Bayes Classification
sklearn.naive_bayes
GaussianNB
Decision Tree Classification
sklearn.tree
DecisionTreeClassifier
Random Forest Classification
sklearn.ensemble
RandomForestClassifier
Polynomial Regression
sklearn.preprocessing
PolynomialFeatures
Support Vector Regression (SVR)
sklearn.svm
SVR
Decision Tree Regression
DecisionTreeRegressor
Random Forest Regression
RandomForestRegressor

4. Datasets (selected) Speed_Camera_Vi olations_6.csv on 14.8MB 112606
Name
Size
Rows
Speed_Camera_Vi
olations_6.csv on
14.8MB
112606
olations_14.json
39.9MB
Crimes_-
_2001_to_present
_19.csv
1.4GB
Taxi_Trips_17.csv
41GB
reviews_Automoti
ve_5.json
14MB
20473
Tools_and_Home_
Improvement_5.js
107MB
134476

5. Why Cloud? No need to procure and maintain hardware
Do not pay for idle == Pay for what you use only
Scalability
Easy to
create more powerful machine/cluster
Increase disk size
Run several tasks independently
Maintainability
No patching
Software installation (almost)

6. Why AWS (Amazon Web Services)
Leading Cloud Provider (Gartner)
Most
Features
Services
The oldest (11 years old)
Automation
Easy to use
Secure

7. AWS EC2 Instance Types (what does it mean?)
General Purpose
Compute Optimized
GPU Instances
Memory Optimized
Storage Optimized
Example:
Name
vCPU
ECU
Memory (GiB)
Instance Storage (GB)
Price
i3.large 2 7
15.25
1 x 475 NVMe SSD
$0.156 per Hour
i3.xlarge 4 13
30.5
1 x 950 NVMe SSD
$0.312 per Hour
i3.2xlarge 8 27 61
1 x 1900 NVMe SSD
$0.624 per Hour

8. What do we do? Run assorted Collect analytical data
ML methods
on big datasets (different formats)
on AWS instances
Collect analytical data
in NoSQL DynamoDb
Run regression methods
on collected analytics to
Predict time
Recommend Instance

9. Architecture EBS DynamoDB CLI IAM EC2 Deep Learning AMI S3
Cloud Formation

10. Challenges Overwrite AWS defaults Data Cleaning Systematic approach
Disk sizes
Instance number limits
Data Cleaning
Verify, filter nulls and errors
Systematic approach
All instances
New instance integration
Results Expandability

11. Languages and Tools Python Cloud Formation Boto3 – AWS integration
Matplotlib – graphing
Sklearn – ML methods implementations
Nltk – natural language processing
Pandas – csv processing
Ijson – json processing
Numpy – scientific computing library
Cloud Formation
automation

12. Limitations CloudFormation does not support loops Solution
Spawn multiple CloudFormation stacks from loop in python
AWS provides instance launch limits
Create AWS service ticket to increase limits
Wait till the limit is increased
AWS console does now allows to remove multiple CloudFormation stacks
Run python program that destroys multiple stacks

13. Optimization and Automation
CloudFormation
AWS service – (json or yaml)
Pass parameters
Spawn instances
Pass shell scripts
Launch python jobs
Attach
Disks
Images
Security Groups
Define output resources
Terminate instance upon test completion

14. CloudFormation example (yaml)
Resources: EC2: Type: "AWS::EC2::Instance" DeletionPolicy: Delete Properties: ImageId: ami-228dbc34 InstanceType: !Ref InstanceTypeParameterG SecurityGroupIds: - sg-6d413ccc KeyName: testkey Outputs: instance: Description: Created Instance Value: !Join ["", [!Ref InstanceTypeParameterG, ' ip ', !GetAtt EC2.PublicIp, ' reg ', !Ref "AWS::Region"]]

15. Destroy stacks from python
import time import sys import boto3 client = boto3.client('cloudformation',region_name='us-east-1', aws_access_key_id=‘AAAKK2HPQ', aws_secret_access_key=‘dasfdag/Gv8oKrsdg') total=0 response = client.list_stacks() for sum in response['StackSummaries']: print('Destr ', sum['StackName']) response = client.delete_stack( StackName=sum['StackName'] )

16. Implementation
Each run includes the following steps for each data file
Clean data
Run machine learning methods on data
Create model
Split data into test and train
Fit
Predict
Record detailed timing results into DynamoDB for each ML method
Load
Processing
Preprocessing
Generate graphs on collected data using regression
Expected time

17. Result Processing Once data is collected
Use analytical methods to predict
Execution time
Price
New test will just extend DynamoDB data
Predictions more precise
Clustering
Data is divided into clusters and graph is created
NLP Classification
Model is trained and Test set reviews are classified
Confusion matrix created (5 start – 5x5 matrix)
Correct predictions ratios are saved
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18. Results

19. Results

20. Results

21. Results

23. Lessons Learnt Nltk is slow for stemming
3-4 mins for 1000 reviews
Large Memory constraints for training
Hierarchical Clustering
DBSCAN
AgglomerativeClustering
Prefer using csv files to json
Optimize code
Be skeptical about online examples
Experiment
Automate when possible

24. Future Work Serverless Microservices Neural Networks NLP
Lambda
Neural Networks
TensorFlow
MxNet
Torch
NLP
Word databases
Spark clusters