In Machine Learning Project development, Model deployment is also very important. Hence the developer should have sound knowledge of model deployment in order to expand his contribution to the ML Project development life-cycle. Then only he/she will able to upscale the productive skills. In this small presentation, a brief introduction to ML/DL model deployment is given.

1. STUDENT NAME: OMKAR GURAV
ROLL NO. 30 (PIMPRI)

2. WHAT IS MODEL DEPLOYMENT?
• MAKING YOUR MACHINE LEARNING MODEL AVAILABLE FOR END
USER.
• CREATE A PRODUCT BASED ON MODEL PREDICTION.
• PROVIDING SERVICE TO OTHER ORGANIZATIONS.
• GIVE PREDICTION/ CLASSIFICATION/ DETECTION AS A RESPONSE

3. WHY DEPLOYMENT IS IMPORTANT?
• USER SHOULD BE ABLE TO USE MODEL DIRECTLY.
• TO INCREASE THE ACCESSIBILITY.
• TO LET ALGORITHM WORK ON THE GO.
• DEPLOYMENT IS PORTABILITY.

4. HOW TO DEPLOY?
•CLOUD SERVICES
•WEB APPLICATIONS
•APPLICATION PROGRAMING
INTERFACE
•WEB SITES

5. CLOUD
• GOOGLE CLOUD PLATFORM
• AMAZON WEB SERVICES
(EC2, SAGEMAKER, NETBEANS)
• MICROSOFT AZURE
• IBM BLUEMIX
• SALESFORCE EINSTEIN

6. CONTAINERS
• DOCKER
• KUBERNETS
• AWS FARGATE
• MS AZURE CONTAINER
INSTANCE

7. SERIALIZATION/ DESERIALIZATION
-: SAVING TRAINED MODEL IN FILE AND LOADING
AT THE TIME OF PREDICTION :-
>>> ACCURACY > PERFORMANCE
• PICKLING – PICKLE , JOBLIB
• WEIGHTS & BIAS – TENSORFLOW, KERAS,
PYTORCH, MXNET

8. MORE TOOLS
•FLASK --- WEB API/APP
•DJANGO --- WEB SITES
•HEROKU --- FREE CLOUD SERVER
•GITHUB --- PLATFORM AS A SERVICE

9. WHY WE NEED CLOUD FOR DEPLOYMENT
• CLOUD SERVERS FOR HOSTING WEB APPLICATION,
WEB SITES
• SIZE OF THE TRAINED ML/ DL MODELS.
• FOR VIRTUAL INFRASTRUCTURE

10. CAN WE DEVELOP ML MODELS ONLINE?
• GOOGLE COLAB – NOTEBOOK WITH GPU (FREE 12 HRS)
• KAGGLE KERNEL – DATASET WITH NOTEBOOK / IDE
WITH GPU (FREE 30 HRS)
• AMAZON FORECAST – WITH DATASET
• MS AZURE NOTEBOOKS – IDEAL FOR ML PIPELINE

11. WHAT WE LEARNED !
•UNDERSTOOD DIFFERENT ASPECTS OF PIPE
LINE OF ML PROJECTS
•IMPORTANCE OF CLOUD
•BOTH DEVELOPMENT AND DEPLOYMENT
ONLINE