Machine Learning In Production

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Samir Bessalah

Deploying Machine Learning in Prod

Data & Analytics

Running
Machine Learning Applications
In Production
Sam BESSALAH
@samklr

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

Production Requirements :
- Flexibility and agility
- Scalability and Performance
- Enable Real time decision making, sometimes at huge QPS at subseconds
pace.
- Security

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
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?

- Trained Models are stored in PMML files
- They serve their models via Openscoring

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.

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

A/B Testing - Multi-armed Bandit
Dataiku

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?

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