The document discusses building machine learning models on the cloud. It describes the general steps as defining the problem, preparing and analyzing the data, evaluating different algorithms to improve results, and presenting the final model. Specific examples covered include binary classification of gender and using the Iris dataset to classify flower types by evaluating algorithms like logistic regression, linear discriminant analysis, and KNN. The document demonstrates deploying a model by dockerizing it and deploying on Kubernetes to scale on any cloud. It concludes with providing contact details for any questions.

1. BUILDING MACHINE
LEARNING MODELS ON
CLOUD
VISHWANATH SHANKAR
25/01/2020

2. GENERAL ETIQUETTE
MOBILES ON SILENT
MODE.
QUESTIONS AT THE
Q&A SESSION.

3. MODEL
• WHAT IS A MODEL
• Y = F(X)

4. MODEL LIFECYCLE
TTD
Train Test Deploy

5. STEPS IN
BUILDING
ML
MODEL
Problem definition E.g. male, female classification
Prepare data
Identify the features
Height, weight
Evaluate algorithms
First
Second
Improve results
Present results

6. MALE FEMALE
CLASSIFICATI
ON

7. IRIS DATA SET PROBLEM

8. IRIS
DATA
SET
PROBLEM
Problem definition Identify the flower type
Prepare data Petal and sepal height and length
Evaluate algorithms
Logistic regression
Linear discriminant analysis
KNN
Improve results Select the best accurate model
Present results

9. IRIS DATA SET

10. IRIS DATA SET MODEL
• CHOOSE X AND Y
• SPLIT DATA TO TRAINING AND TEST SET
• EVALUATE DIFFERENT MODELS
• FINALIZE A MODEL
• PARAMETERISE THE MODEL FOR I/P AND O/P
• SAVE THE MODEL FILE

11. DEMO
• PYTHON APP.PY

12. • DOKERIZE THE MODEL
• DEPLOY ON KUBERNETES FOR SCALE
• RUN ON ANY CLOUD
MODEL DEPLOYMENT

13. DEMO
• SAVE THE MODEL
• PYTHON SVM.PY
• DOCKERIZE THE MODEL
• PYTHON SERVER.PY
• RUN THE APP WITH DIFFERENT DATA POINTS
• REACT APP

14. Q&A AND CONNECT
• LINKEDIN:
HTTPS://WWW.LINKEDIN.COM/IN/VISHWANATH-
SHANKAR-8233418/
• TWITTER: @SVISHWA787
• GITHUB:
HTTPS://GITHUB.COM/VISHWANATHSHANKAR/IRI
S-IMAGE-CLASSIFICATION