Select ML from Databases.pdf

Confidential and Proprietary. Do not distribute without Couchbase consent. © Couchbase 2021. All rights reserved.
Select ML from Databases
Nithish Raghunandanan
@nithishr
26th May 2022

Agenda
Fill in agenda items and numbers
Background
ML in Databases
Demo: ML in Couchbase
01/
01/
02/
03/
04/ Conclusion

Confidential and Proprietary. Do not distribute without Couchbase consent. © Couchbase 2021. All rights reserved. 3
1 Background

Background
A Journey into Machine Learning Capabilities in Databases
• Background in Data/ML Engineering
• Built Machine Learning Models for
Startups
• Developer Advocate @ Couchbase
• Explore the ML Capabilities of Databases
@nithishr

● Extract Meaningful Insights from
Data
● Solve Business Problems
● Personalized Offers
● Churn Prevention
● Anomaly Detection
● Lots More
Why ML?

Common Workflow
Get Training Data
● Transactional
Databases
● ETL Jobs
● Data
Engineers
Feature
Engineering
● Data
Wrangling
● ETL Jobs
● Feature
Stores
● Data
Scientists +
Engineers
Train Model
● Algorithms
● Data
Scientists
Call Model
from Apps
● Applications
call ML
Service
● Backend +
Data Engineer
+ Data
Scientist
Deploy Model
● Package
Model
● Microservice
to Serve
Model
● Data Engineer
+ Scientist

Pros
● Works Well
● Scale Services Independently
Common Workflow
Cons
● Data Duplication
○ Transactional Database
○ Feature Stores / Copies of Data
● Non Trivial ETL Jobs
● Updating Data / Models
● Combined efforts of Data Engineers,
Scientists & Backend Engineers

2 Machine Learning in
Databases

● Inference using SQL
● Built In Machine Learning Methods
○ BigQuery, Redshift
● Third Party Additions to Existing Databases
○ MindsDB
● Support for Custom Machine Learning Models
(Python/R)
○ SQL Server, Oracle, Couchbase, Firebase
Database Support for AI

● Set of Built In ML algorithms for Most Use Cases
● Optionally import custom models
● Workflow
○ CREATE Model
○ SELECT Model(Inputs) as Inference from
Database
Built In Machine Learning Methods

MindsDB
● Pluggable ML Support for Existing DBs
● Connect to underlying DB API
● Workflow
○ CREATE PREDICTOR from DB
○ SELECT Outputs FROM Model where data={}
as Inference from Database
Third Party Additions to Databases

● Support for Custom Models in Python, R.
● Workflow
○ Build Model on Dev Machines
○ Package the Model
○ Create a Custom Procedure in DB
○ SELECT Procedure(Inputs) as Inference from
Database
Custom Machine Learning Methods

Feature Built in ML Third Party
Integrations
Custom ML
Methods
Flexibility Limited Limited High
Explainability Low Low - Medium High
Target Audience Data Analysts /
Engineers
Data Analysts /
Engineers
Data Scientists
Summary

Pros
● Reduce ETL: DB single source of truth
● Data does not leave DB storage
● Out of the box Models
● Easy Upgrade Path for Models
● No external Microservice for Model
Serving
Summary
Cons
● Flexibility
● Ensure Performance
● Explainability

3 Demo: ML in Couchbase

● Massively Parallel
Processing(MPP)
Architecture
● Custom User Defined
Functions(UDFs) in Python
● ML Inference in SQL
Statements
Machine Learning in Couchbase
Couchbase Analytics

● Insurance Quotes
● Custom Quotes based on Estimated Medical
Costs
● Features: Age, Gender, BMI, Smoking,
Region, Children
● Customer gets real time estimates
Machine Learning in Couchbase: Demo

Steps
1. Train Model in Python locally
2. Package the Model into Python Library
3. Deploy Library to Couchbase Analytics
4. Create User Defined Function to use Python
Library
5. Model Inference using SQL statements
Instant Quote Demo
OUTPUT
Machine Learning Model
{
"quote": 19300.40
}
INPUT

Machine Learning in Couchbase: Instant Quote Demo

Real Time Machine Translation Demo
Real time localization of content

● Pre-trained model from Hugging
Face
● Multi Language Support
● 300-400 MB / Model
● 1000+ models
● Used by Microsoft Translator

Demo

● Create Custom Models in Python
● Run Inference directly using SQL in Workbench, SDK or Couchbase
REST API
● Scale by increasing resources
● Update Models by Redeploying
● Reduced Infrastructure: Eliminate ML Microservices & ETL Jobs
Machine Learning in Couchbase: Summary

4 Conclusion

● Machine Learning Integration into Databases
● Models are becoming more flexible
● Not Suitable for all use cases
When to Use?
● Out of the Box Solutions
● Simplified Model Deployment Workflows
● Reduced Infrastructure: ETL Jobs & ML Services
Machine Learning in Databases

Web
Twitter
Facebook
couchbase.com
twitter.com/couchbase
facebook.com/couchbase
Nithish Raghunandanan
@nithishr
https://github.com/nithishr/machine_learning_couchbase/
https://github.com/couchbaselabs/neural-translation-example/
CONTACT

THANK YOU

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