scaling machine learning @ careem-care… · predict etas (expected time of arrival) ... feature...
TRANSCRIPT
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Scaling Machine Learning @ Careem
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Who?
Ahmed Kamal- Machine Learning Platform Lead @ Careem, - Computer Engineer by training
I blog @ ahmedkamal.me and tweet @_akamal_
Representing the work of Yoda team and other awesome colleagues @ Careem
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Who are we?
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Careem was founded in 2012,in Dubai, UAE...
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Our mission is to simplify and improve the lives of people and build an awesome organization that inspires
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Multi-vertical platform of Mobility, Delivery, Payments
15 Countries 120 Cities 3,500 Colleagues
33M Customers 1M Captains
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We are the leading technology platform in the middle east !
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AI Use cases we have at Careem
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● Predict demand and supply
○ Essential for peak and dynamic pricing
○ Plan ahead while dispatching
● Fulfill our promise to customers and captains
○ Predict ETAs (Expected time of arrival)
○ Predict prices
● Predict customer and captain’s behavior
○ Predict cancellations
○ Predict captain acceptance
● Platform Integrity
○ Fraud Prevention and Detection
○ Anomaly Detection
Sneak Peek into ML @ Careem
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Example
6 min
MaryamCaptain Oussama
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Example
6 min
8 minCaptain
Oussama
Maryam
Rashid
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Example
6 min
8 min
Captain Oussama
Maryam
Rashid
Proba(Cancellation) = 0.8
Proba(Cancellation) = 0.1
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Demand prediction (Time Series Forecasting)
Reality Prediction
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You are now one of us
14
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Let’s Solve a Problem !
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Estimated Time Arrival
Problem Definition
Calculate the post assignment ETA for a captain for specific booking accurately in real time
Problem Type
Regression
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user_id : 1car_type : “Business”
captain_long": 55.1486643,captain_lat": 25.0911563,
booking_long": 54.1506833,booking_lat": 25.084343
Dataset Sample
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R&D workflow
Get Data
Train Model
PersistModelModeling
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ML in real world (or why do we need a MLE)
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Data is huge :)
ML in real world (or why do we need a MLE)
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Data is huge :)
ML in real world (or why do we need a MLE)
Feature Engineering takes long time
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Data is huge :)
ML in real world (or why do we need a MLE)
Feature Engineering takes long time Training needs lots of resources
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Data is huge :)
ML in real world (or why do we need a MLE)
Feature Engineering takes long time Training needs lots of resources
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Data is huge :)
ML in real world (or why do we need a MLE)
Feature Engineering takes long time Training needs lots of resources
Needs to be on Production
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Data is huge :)
ML in real world (or why do we need a MLE)
Feature Engineering takes long time Training needs lots of resources
Build an API
Needs to be on Production
Low Latency & High Throughput
Monitoring & Alerting
Fault Tolerance & Autoscaling
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How much time ?
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Congrats on the amazing model !
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Continuously refresh and updated deployed models
Post Deployment Challenges
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Continuously refresh and updated deployed models
Post Deployment Challenges
Performance Monitoring and alerting
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Continuously refresh and updated deployed models
Post Deployment Challenges
Performance Monitoring and alerting
A/B Testing between new/old or new/new models
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Continuously refresh and updated deployed models
Post Deployment Challenges
Performance Monitoring and alerting
A/B Testing between new/old or new/new models
Which cities are ready for ML ?
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Continuously refresh and updated deployed models
Post Deployment Challenges
Additional 119 models ?
Performance Monitoring and alerting
A/B Testing between new/old or new/new models
Which cities are ready for ML ?
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Continuously refresh and updated deployed models
Post Deployment Challenges
Additional 119 models ?
Performance Monitoring and alerting
A/B Testing between new/old or new/new models
Too many APIs ? Integration headache !Which cities are ready for ML ?
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Is this real ! Google says, yes :)
https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf
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Yoda
by
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Yoda
● Our mission as Yoda team: Accelerate the adoption of AI at Careem.
● We are building an in-house Machine Learning Platform, to automate the end to end experience.
● To enable expeditious development and deployment of 1000+ of ML models in production
Serve modelsMonitoring and alerting
Fetch dataClean dataFormat data
Feature engineeringBuilding modelsEvaluating models
Machine learning cycle
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Your Life After Yoda
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Dataset Generation
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Model Training
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40
Model Report
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- Configuration Based Deployment service enabling deployment in few minutes.
- Latency, integration and API tests
- Auto-Rollout Capabilities to smoothen model updates experience
One Click Deployment
Configs Production Level APIOne Click !
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URL => eta-service.yoda.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
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Model Serving
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Summary of Yoda
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• Enrich Farabi DB (Feature Store)
• Auto-ML
• AI for everyone
• Time Series Forecasting
• Anomaly Detection
• Natural Language Processing
What’s Next for Yoda?
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Thank you! Danke! Teşekkürler!شكراً! شکریہ!
For Slides : @_akamal8_