Scaling ml @ careem (oreilly ai conf)
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This document summarizes Ahmed Kamal's presentation on scaling machine learning at Careem. Careem uses machine learning to enhance customer and driver experiences, ensure platform integrity through fraud detection and anomaly detection, and improve demand and supply forecasting. To scale ML usage across Careem's operations, the company built a machine learning platform to automate the end-to-end ML workflow from problem formulation to model deployment. The platform reduces costs, speeds up development and increased the impact of ML compared to doubling their data science team size. Automating tasks like dataset generation, model training and serving APIs allows more models to be deployed and non-experts to benefit from ML.
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Scaling ml @ careem (oreilly ai conf)
- 2. Who? Ahmed Kamal - Tech Lead @ Machine Learning Platform, Careem, - Computer Engineer by training I blog @ ahmedkamal.me Find me on twitter @_akamal_ Presenting the work of my team and other awesome colleagues @ Careem
- 3. Agenda 3 Introduction AI @ Careem Motivation behind building ML Platform A Deep dive into Careem MLP Scaling Machine Learning Usage Learnings from the journey 1 2 3 4 5 6
- 4. We are the leading technology platform in the middle east !
- 5. Multi-vertical platform of Mobility, Delivery, Payments 14 Countries 100+ Cities 3000 Colleagues 33M Customers 1.7 M Captains
- 6. To simplify and improve the lives of people… ...and build an awesome organisation that inspires
- 7. Sneak Peek into ML @ Careem ● Enhances customers and captains Experience ○ ETAs & Accurate Prices ○ Cancellations & Captain Acceptance
- 8. Sneak Peek into ML @ Careem ● Enhances customers and captains Experience ○ ETAs & Accurate Prices ○ Cancellations & Captain Acceptance ● Platform Integrity ○ Fraud Prevention and Detection ○ Anomaly Detection
- 9. Sneak Peek into ML @ Careem ● Enhances customers and captains Experience ○ ETAs & Accurate Prices ○ Cancellations & Captain Acceptance ● Platform Integrity ○ Fraud Prevention and Detection ○ Anomaly Detection ● Ensure efficiency of our two-sided marketplace ○ Demand and Supply forecasting ○ Smart Dispatching & Peak
- 10. Building an AI ecosystem 10 ML Infra Scalable Machine Learning Platforms Data Warehouses Well governed, trustworthy and documented data Big Data capabilities Easy and reliable access to large volumes of data Scalable AI Ecosystem Know How AI aware colleagues
- 11. Expectation: (%) Reality: (%) Formulate the problem Data selection and feature engineering ML model development Model deployment, integration and monitoring ML Workflow - Challenges of ML at scale
- 12. ML Challenges
- 13. ML Development Challenges 13 Lots amount of data to process Prepare Data Training is very costly and takes long time. Reproducibility Train a model Development environment mismatch with production environment. Transfer to Prod Models needs to be in production - Low Latency & High Throughput - Monitoring & Alerting - Fault Tolerance & Auto scaling Deploy
- 14. Post Deployment Challenges Continuously refresh and updated deployed models
- 15. Post Deployment Challenges Performance Monitoring and alertingContinuously refresh and updated deployed models
- 16. Post Deployment Challenges Performance Monitoring and alertingContinuously refresh and updated deployed models A/B Testing between new/old or new/new models
- 17. Post Deployment Challenges Which cities are ready for ML ? Performance Monitoring and alertingContinuously refresh and updated deployed models A/B Testing between new/old or new/new models
- 18. Additional 100 models ? Post Deployment Challenges Which cities are ready for ML ? Performance Monitoring and alertingContinuously refresh and updated deployed models A/B Testing between new/old or new/new models
- 19. Additional 100 models ? Post Deployment Challenges Which cities are ready for ML ? Performance Monitoring and alerting Too many APIs ? Integration headache Continuously refresh and updated deployed models A/B Testing between new/old or new/new models
- 20. Careem ML Platform Accelerate adoption of AI @ Careem Automate the end to end ML experience
- 21. Formulate the problem Select data and feature engineering Deploy model to production Train and test models Monitor and improve ML lifecycle Tackling ML Life Cycle
- 23. Model Training
- 24. Model Report
- 25. Batch Serving - Batch predictions generated offline for a target dataset. - Store predictions in different data-stores.
- 26. Realtime Serving - Config Based Modular Serving Framework - Inject Custom Feature Engineering Logic - Access to external data & A/B testing support - Prediction and performance logging -
- 27. - Configuration Based Deployment service. - Latency, integration and API tests - Auto-Rollout Capabilities to smoothen model updates experience One Click Deployment Configs Production Level API One Click !
- 28. A Glued Dynamic System Data Generation Model Training Model Serving
- 29. URL => eta-service.careem.com/v1/101/eta Request => [{"uuid": "9fdsaf9as9da9sd9", "assignment_time": "2019-03-14 14:09:46", "captain_lat": 30.0039, "captain_long": 31.1422, "booking_lat": 30.0022, "booking_long": 31.1405}] Response => { "response": [{ "uuid": "9fdsaf9as9da9sd9", "prediction": 2.5 } ], "result": "ok" } Now you have an API
- 30. Impact Infra Cost Reduced time needed by a DS from days to minutes per model. Reduced time needed by a DE from 2 weeks to 12 minutes per use-case One-Click Serving API Deployment Training pipelines and Auto-rollout Dataset Generation More impact than doubling the size of our DS team Serverless Job Training Up to 90% saving on training cost We are able to have more models on production and have much higher compounded impact Model Reports (Visualizations + Metrics over time) Saving hours from DS time Reduced analysis and evaluation time from hours to minutes. Productivity
- 31. To Summarize
- 32. Scaling Machine Learning Usage
- 33. From Scaling Infra to Scaling Usage AI For Everyone Auto Machine Learning Custom AI Powered Toolings Generic Time Series Forecasting Supply Forecasting Demand Forecasting Campaign Management System Customer Care
- 34. - Enables experimenting with ML in short time. - Lower the barrier for using ML for lots of people AutoML Auto Feature Selection/Engineering Auto Hyperparameter Tuning Auto Model Selection Auto Model Ensembling Auto Rollout Rich Feature Store
- 35. - ML development is a cross functional work. - Heavy investments in automation is the key for scaling ML. - Design with different user segments in mind. Learnings from the journey
- 36. For Slides : @_akamal8_
- 38. Thank you! Danke! Teşekkürler! !ﺷﮑرﯾہ !ًاﺷﻛر For Slides : @_akamal8_