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Machine Learning In Production

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Samir Bessalah
Samir Bessalah

Deploying Machine Learning in Prod

Data & Analytics
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Running Machine Learning Applications In Production Sam BESSALAH @samklr
Might Works well for KAGGLE!
Might Works well for KAGGLE! But Kaggle isn’t real world Machine learning!
In Real Life - Trade off : Accuracy vs Interpretability cs Speed vs Infrastructure contraints - Interpretability and Speed often beats accuracy - Most of the time Kaggle is a feature engineering contest - Contest oriented vs Real Product Impact
But in real life … Things are less obvious Data Engineers Data Pipeline Data Scientists / ML Engineers APP Applications Developers
But in real life … Things are less obvious Data Engineers Data Pipeline Data Scientists / ML Engineers APP Applications Developers Innovation is often (wrongly ?) thought to be here ...
http://www.slideshare.net/jssm1th/an-architecture-for-agile-machine-learning-in-realtime-applications
@josh_wills
josh_wills
Production Requirements : - Flexibility and agility - Scalability and Performance - Enable Real time decision making, sometimes at huge QPS at subseconds pace. - Security
josh_wills
Machine Learning as a Software Problem - Most ML developement patterns lead to software design anti patterns - Dependencies in code, creeps through Models dependencies in Data - Wasteful use of data, since most ml model selection require multiple version of data. Hence the instability of data, and of prediction services - Breaks system isolation, leading to un-maintainable stacks
In Production, Machine Learning is a Software and System Problem. Treat it accordingly !!!!
Deployment / Model Serving
Deployment / Model Serving The Missing Part in ML
- Model Serving is often ignored or left out to Back End Engineers to implement at their own liking. - More often it involves serving an API or a Service to do the Predict function. But that not often enough. - Software scaling can become problematic to the accuracy of the model. - How many models are you serving? - Are you running something else ? - Are you updating your model in real time?
Example : AirBnb
- Trained Models are stored in PMML files - They serve their models via Openscoring
PMML ?
PMML? - Might be the solution for some (most ?) cases - Support many models, but lacks support for many others - Fails to capture the evolution of your modeling process … Transformations, re encoding, etc . - Better suited for exporting models to other systems, rather than being served to machine learning products with real user facing. - And … XML ?? Really????
Model Versioning - Packaging - You usually don’t serve only one model. But a lot more. Especially when running experiments. - You should vie to package your model in versionned way. - Git is awesome, but not appropriate for live model serving - Build a model repository or a model index - I usually use fast KV store or advanced data stores to save my models - Build a service to manage your models (Model Manager) responsible for evaluating and updating your model.
TensorFlow Serving
Serialization - Remember PMML ? - In Big Data, data has schema and proper evolution? - Why not models ? - Lots to choose from : Protobuf, Avro - Use binary schema to represent and version your models
Evaluation - Business metrics often differ from core model metrics : Trade off between long term metrics and short term metrics. - Hyperparameters - A/B Testing - Multi Armed Bandits Problem
Hyper Parameters Netflix
A/B Testing - Multi-armed Bandit
A/B Testing - Multi-armed Bandit Dataiku
Experiments
Reproducibility - How to keep track of data used for training ? - Are notebooks enough? - Junpyter Notebooks, Spark Notebooks, Zeppelin, etc …. - Need for an end to end solution. Not perfect, but a workable one.
I forgot many things - Monitoring - Pipeline tuning (one model is often fed to another one) - RPC over REST for fast model serving ? - How to deal with heterogeneous systems ? - Do you really have to distribute your processing? - Is more data better than smartly tuned algorithms?
Machine Learning In Production

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Machine Learning In Production

  • 1. Running Machine Learning Applications In Production Sam BESSALAH @samklr
  • 2.
  • 3.
  • 4.
  • 5. Might Works well for KAGGLE!
  • 6. Might Works well for KAGGLE! But Kaggle isn’t real world Machine learning!
  • 7.
  • 8. In Real Life - Trade off : Accuracy vs Interpretability cs Speed vs Infrastructure contraints - Interpretability and Speed often beats accuracy - Most of the time Kaggle is a feature engineering contest - Contest oriented vs Real Product Impact
  • 9. But in real life … Things are less obvious Data Engineers Data Pipeline Data Scientists / ML Engineers APP Applications Developers
  • 10. But in real life … Things are less obvious Data Engineers Data Pipeline Data Scientists / ML Engineers APP Applications Developers Innovation is often (wrongly ?) thought to be here ...
  • 12.
  • 15.
  • 16. Production Requirements : - Flexibility and agility - Scalability and Performance - Enable Real time decision making, sometimes at huge QPS at subseconds pace. - Security
  • 17.
  • 19.
  • 20. Machine Learning as a Software Problem - Most ML developement patterns lead to software design anti patterns - Dependencies in code, creeps through Models dependencies in Data - Wasteful use of data, since most ml model selection require multiple version of data. Hence the instability of data, and of prediction services - Breaks system isolation, leading to un-maintainable stacks
  • 21. In Production, Machine Learning is a Software and System Problem. Treat it accordingly !!!!
  • 22.
  • 23.
  • 24.
  • 26. Deployment / Model Serving The Missing Part in ML
  • 27. - Model Serving is often ignored or left out to Back End Engineers to implement at their own liking. - More often it involves serving an API or a Service to do the Predict function. But that not often enough. - Software scaling can become problematic to the accuracy of the model. - How many models are you serving? - Are you running something else ? - Are you updating your model in real time?
  • 29.
  • 30. - Trained Models are stored in PMML files - They serve their models via Openscoring
  • 31.
  • 33.
  • 34. PMML? - Might be the solution for some (most ?) cases - Support many models, but lacks support for many others - Fails to capture the evolution of your modeling process … Transformations, re encoding, etc . - Better suited for exporting models to other systems, rather than being served to machine learning products with real user facing. - And … XML ?? Really????
  • 35.
  • 36.
  • 37. Model Versioning - Packaging - You usually don’t serve only one model. But a lot more. Especially when running experiments. - You should vie to package your model in versionned way. - Git is awesome, but not appropriate for live model serving - Build a model repository or a model index - I usually use fast KV store or advanced data stores to save my models - Build a service to manage your models (Model Manager) responsible for evaluating and updating your model.
  • 38.
  • 39.
  • 41.
  • 42.
  • 43. Serialization - Remember PMML ? - In Big Data, data has schema and proper evolution? - Why not models ? - Lots to choose from : Protobuf, Avro - Use binary schema to represent and version your models
  • 44. Evaluation - Business metrics often differ from core model metrics : Trade off between long term metrics and short term metrics. - Hyperparameters - A/B Testing - Multi Armed Bandits Problem
  • 46.
  • 47.
  • 48. A/B Testing - Multi-armed Bandit
  • 49. A/B Testing - Multi-armed Bandit Dataiku
  • 51.
  • 52. Reproducibility - How to keep track of data used for training ? - Are notebooks enough? - Junpyter Notebooks, Spark Notebooks, Zeppelin, etc …. - Need for an end to end solution. Not perfect, but a workable one.
  • 53. I forgot many things - Monitoring - Pipeline tuning (one model is often fed to another one) - RPC over REST for fast model serving ? - How to deal with heterogeneous systems ? - Do you really have to distribute your processing? - Is more data better than smartly tuned algorithms?