This document summarizes the benefits of building an in-house machine learning platform called Positron. Key points: - Positron allows for quick and consistent model deployments, simplified model management, experiment tracking, and efficient workflows. - It features a multi-model pipeline for seamless model creation and validation. Models can be deployed with minimal configuration. - The platform uses MLeap for model serialization/deserialization, which provides portability and fast performance without dependencies on specific frameworks. - It aims to provide low latency and high throughput predictions, while allowing for customization and integration with existing infrastructure. External and internal models can be easily deployed.

1. Benefits of a Homemade ML
Platform.
Bartosz Chodnicki & Liniker Seixas

2. Who?
Data Science & Engineering Lead
Search, Spam & Assets @
Truecaller
Our goal is to protect users from unwanted & unsolicited
communications driven by actionable intelligence and data
informed decisions
Senior Software Engineer
Search, Spam & Assets @
GetInData <Truecaller

3. Why do you need ML-Ops?
Quick &
Consistent
Deployments
Simplified
Model
Management
Versioning &
Experiment
Tracking
Efficient
Workflows &
Fast Results
A/B Testing
Automated
Model
Tuning

4. ● Multi-ML modelling pipeline
● Enabling seamless creation and validation of machine learning
models
● Minimalistic configuration based design
● Out of the box ability to deploy external & internal models
● Serve different model flavours
● Online and offline feature stores
What is Positron?

5. Tech
Stack.

6. Search, Spam &
Assets
Flow.

7. Goals.
Build
&
Solve
Low Latency
& High
Throughput
Customizable
&
Integration with
Current
Infrastructure
Support
Internal &
External
Models
Easy Model
Deployment
&
Minimalistic
Code

8. ● From idea to the orbit in 3 months
○ Keep it simple
○ Don’t know all the requirements at beginning
○ Make generic what change for sure, but don’t implement all at once
● Reuse what you can
○ ML model serializer - MLeap
○ Existing TC development stack
○ ML models -
https://github.com/GantMan/nsfw_model!
● Improve model deployments
○ Build, test, fail & repeat
○ Data scientists shipping models with a snap
Agile Rocket Development.

9. MLeap.
● Benefits
○ Build data pipelines and train algorithms with Spark and
Scikit-Learn
○ ML Pipelines serialization/deserialization to/from a common
framework (Bundle.ML)
○ No dependencies on Spark or Scikit (numpy, pandas, etc)
○ 2 portable serialization formats (JSON, Protobuf)
○ Lightning fast!
● Supported Model Flavours
○ Scikit Learn
○ Spark/PySpark MLLib/ML
○ Tensorflow/Keras

10. Lightspeed.
● Demand of low latency
● Using GRPc
○ Fast (vs JSON)
○ Lightweight - low network traffic costs
● No dependencies on Spark or Scikit (numpy, pandas, etc)
● Optimized to deliver execution of ML Pipelines in
microseconds
● Performance is attributed to supporting technologies like the
Scala Breeze

11. Seldon (Python) vs MLeap Performance.
Python - CSM Model - 10 Trees - 10K Requests
MLeap - CSM Model - 10 Trees - 10K Requests

12. ● Costs of creation may not be
worth benefits
● Usage of some open source
libraries which will not support
needed cases
● Need for a larger team with new
expertise for custom
components
● Self managed platform
Homemade ML Platforms.
Risks Benefits
● Low cost
● Complete customizability &
flexibility
● Open source
● Seamless integration with
in-house services
● Eliminate proprietary
dependencies

13. Future.
● More & more models!
● Integration with Apache Flink for streaming predictions
● Build a common platform for all business units
● Libraries of custom transformers
● Check different serializers (eg PMML, onnx)

14. “To reach stars you need courage and vision”
Thank you
Bartosz & Liniker