John Robert: Making your machine learning model usable by others Data Science Online Camp 2021 Website - https://dscamp.org/ Youtube - https://www.youtube.com/startuplviv FB - https://www.facebook.com/Data-Science-Camp-103012708431833

1. Making your Machine
Learning Model
useable by others

2. Hello!
I am John Robert
I put machine learning in production at Adtriba.
Adtriba uses machine learning to improve marketing, optimize
budget allocation and increase revenue
@trojrobert
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3. Usability
Deploy machine learning models to production
Make a product or service with machine learning
model
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4. Examples of usable models
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From ‘Attention Is All You Need’ by Vaswani et al.

5. Examples of usable models
Tesla Autopilot
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6. Machine Learning Life Cycle
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Source - ttps://woolpert.com/media/blogs/geospatial/what-does-a-machine-learning-project-look-like/

7. Usable Model can
be compared with
serving prepared
food.
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Whole roasted scup with piperade, grilled cauliflower and bronze fennel -
https://www.tripadvisor.com/LocationPhotoDirectLink-g41790-d11620864-
i325210489-Feather_Wedge-Rockport_Cape_Ann_Massachusetts.html

8. 8
Source and prepare high quality data Train model Deploy model
Source and prepare high quality ingredients Cook a meal Serve the meal
AI system

9. “Plates” used in serving/deploying ML models
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10. “Table” for serving/ deploying ML Models
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Source - https://allcode.com/cloud-providers/

11. AI Systems = Model(code) + Data
DevOps
MLOps
Normal system = Code
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Source - https://towardsdatascience.com/challenges-deploying-machine-learning-models-to-production-ded3f9009cb3

12. Process of serving/deploying ML Models
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13. Serving/ deploying traditional systems(DevOps)
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14. Tools for serving /deploying ML Models
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create and share beautiful, custom web apps for machine learning
and data science
is continuous integration for machine learning. Bring DevOps
practices to your projects for automatic, reproducible, and fast
machine learning.
Allow you to manage and deploy models from a variety of ML
libraries to a variety of model serving and inference platforms.
end-to-end enterprise AI platform that automates and accelerates
every step of your path from data to value.
end-to-end machine learning platform to build and deploy AI models
at scale
making deployments of machine learning (ML) workflows on
Kubernetes simple, portable and scalable.

15. Tools for serving /deploying ML Models
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16. Example of served/deployed ML Model
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17. Roadblock of deploying ML model (development)
Data Leakage
happens when your
training data contains
information about the
target, but similar
data will not be
available when the
model is used for
prediction.
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18. Roadblocks when serving/deploying ML Model
Data Drift
is the change in input
data distribution of
the model.
Prediction Drift
is the change in
output of the model.
For instance change
in target data
Covariate Drift
is the change in
pattern learnt by the
model. Change in the
relationship between
the features and the
target
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Model Degradation

19. Roadblocks when serving/deploying ML Model
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Source - MIT 6.S191: Evidential Deep Learning and Uncertainty

20. Roadblocks when serving/deploying ML Model
Out Of Distribution
this occur when you try to
predict a class that the
model was not trained on
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What will the model predict when the input image is a
car?

21. Roadblocks when serving/deploying ML Model
Infrastructure
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Source - https://severalnines.com/database-
blog/scaling-postgresql-large-amounts-data
Source - https://akfpartners.com/growth-blog/what-is-latency

22. When your model is too big for the plate
Quantization
Quantization is
reducing the bitwidths
of weights in a model.
For instance convert
values from float to
integer.
Pruning
reducing the size of a
machine learning
model. For instance
removing some
layers.
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Model compression

23. Preserve your served/ deployed ML model
Retraining
Since the world is
changing, data
generated is also
changing. Models
need to adapt to the
changes.
Monitoring
Track and analyse
data and model
performance
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Data and Model Health (DataRobot)