The recently open-sourced platform called Marvin AI is helping B2W Digital data science teams to put some interesting projects into production, such as the dynamic pricing and fraud detection. The platform is helping B2W avoid challenges that are common in machine learning projects, like building a hypothesis prototype and putting it in production, enabling it to handle ever-increasing data sets, and supporting the necessary throughput of the largest e-commerce company in South America. Marvin AI is made up of tools, libraries, an embedded server that exposes microservices to serve and manage models, and a very useful design pattern, DASFE, that simplifies the process of building new machine learning engines. In this 90-minute hands-on tutorial, one of the project architects and main open source contributor will explain all the core features of Marvin.

1. Title of the Presentation Goes Here
© 2018 Carnegie Mellon University
SATURN 2018
14th Annual SEI Architecture Technology User Network Conference
MAY 7–10, 2018 | PLANO, TEXAS
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Marvin AI - An Open Source Platform to
Deploy and Manage Machine Learning Models
Daniel Takabayashi and Jeremy Elster

2. Marvin AI - An open source platform to deploy and manage machine learning models
© 2018 Carnegie Mellon University
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About us…
Daniel Takabayashi
• Technology Manager and Software Architect @ B2W Digital (São Paulo - Brazil)
• Startups Mentor @ Founder Institute (San Francisco - USA)
• Co-Founder of Boolabs, an Brazilian artificial intelligence startup acquired by B2W in 2016
• MSc in Computer Engineering (IPT - Brazil)
and my contacts…
daniel.takabayashi@b2wdigital.com
@DanTakabayashi
linkedin.com/danieltakabayashi
github.com/takabayashi

3. Marvin AI - An open source platform to deploy and manage machine learning models
© 2018 Carnegie Mellon University
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About us…
Jeremy Elster
• Data Scientist @ B2W Digital (Irvine - CA)
• University of California @ Berkeley
and my contacts…
jeremy.elster@b2wdigital.com
linkedin.com/in/jeremyelster/
github.com/jeremyelster

4. Marvin AI - An open source platform to deploy and manage machine learning models
© 2018 Carnegie Mellon University
SATURN 2018
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B2W Digital is the leading e-commerce company in Latin
America.

5. Marvin AI - An open source platform to deploy and manage machine learning models
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Agenda
1.Some Problems in Machine Learning Projects (8 slides)
2.Marvin AI Platform (10 slides)
2.1.Main Components
2.2.Architecture Views
3.Hands On (~ 30 minutes)

6. Marvin AI - An open source platform to deploy and manage machine learning models
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Marvin AI - An open source platform to deploy and manage machine learning models
Some Problems in ML Projects

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Marvin AI - An open source platform to deploy and manage machine learning models
© 2018 Carnegie Mellon University 7
The knowledge domains to
understand, research, build
and deploy ML projects are
huge and distinct.
Building a “team" with
complementary profiles
makes the project more
expensive.
Some Problems in Machine Learning Projects (1 of 5)

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Almost all data scientists
(Type A) do not have the
necessary software
engineer skills to build a
production grade solution.
And good Type B
professionals are unicorns!
Some Problems in Machine Learning Projects (2 of 5)

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The faster the creation
process ends, the sooner the
improvement process starts.
Rapidly establishing a
baseline MVP (around few
weeks) is strategic to the
project success!
Some Problems in Machine Learning Projects (3 of 5)

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Marvin AI - An open source platform to deploy and manage machine learning models
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ML Teams must save the
hypothesis, data, code and
metrics for each new iteration
of the project.
Reproducibility is always a
requirement !!!
Some Problems in Machine Learning Projects (4 of 5)

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Marvin AI - An open source platform to deploy and manage machine learning models
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Code is prototyped locally
or in Jupyter notebooks
(interactive IDE) in any
language.
Models run over test
datasets, but are not
scalable for production.
Some Problems in Machine Learning Projects (5 of 5)

12. How to simplify the process of
exploring, building, testing and
deploying machine learning
projects in a reproducible way?

13. ABSTRACTION
+
STANDARDIZATION

14. github.com/marvin-ai

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Marvin AI - An open source platform to deploy and manage machine learning models
Marvin AI Platform

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© 2018 Carnegie Mellon University
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Marvin AI Platform: General Infos
• Started at B2W Digital in 2016 to solve internal problems
• Released as open source on 09/2017 with Apache 2 licence
• First Paper published in Papis.io conference (Boston) on 09/2017
• Three versions released since 09/2017
• Community is growing…
The project was submitted to the Apache incubation process!

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Marvin AI Platform: Quality Atrributes
For Data Scientists:
• Interoperability - to support different programmer languages
• Usability - to accelerate and simplify the model creation process
For Administrators:
• Manageability - to simplify the distributed deploy/management process
• Scalability - to support from tiny to intensive loads
For Marvin Developers:
• Modificability - to improve and release new versions constantly
• Maintainability - to allow all type of programmer (from beginners to experts) to
contribute

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Marvin AI Platform: Main components (1 of 3)
DASFE*
Data Acquisition, Serving, Feedback and Evaluation

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Marvin AI Platform: Main components (2 of 3)
Engine - Specific language project that contains source code related to the model.
Implementation of DASFE pattern.
Artefacts - Persistent and versioned binaries (initial dataset, dataset, model, and metrics).
Engine Executor - Architectural abstraction implementation around the Engine such as
parallelism, distribution, versioning, rest apis, availability and so on.
Toolbox - Set of CLI's, utilities, classes and libraries, specific per programming language,
that supports the whole process of exploring, developing, testing and deploying an engine
(Eg. python-toolbox, scala-toolbox, r-toolbox etc).

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Marvin AI Platform: Main components (3 of 3)

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Marvin AI Platform: Some Architectural Tactics
Quality Attributes Main Tactics
Interoperability
Using gRPC connections between the EngineExecutor and the
UserCode code and a DSL to describe the interfaces.
Usability
CLI’s with default parameters and Generic Rest APIs to manage and
request everything in the system. Marvin defines external and
coherent concepts (Eg. Executor, Engine, Action and Toolbox).
Manageability
A Manager actor to control (locally or remotely) everything in the
system and a cluster concept to help in distributed installations.
Scalability
Actor model architecture to increase parallelism and distribution
throughout the system. Containerisation as deployment solution.
Modificability
Encapsulation through Actor model and base classes, minimum
responsibility of each actor and a lot of abstraction.
Maintainability
Scala as implementation language, encapsulation, unit tests and
continuous delivery. Virtualized development environment (vagrant
and docker).

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Context
Diagram
Marvin AI Platform: Architecture Views (1 of 4)

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Components
Diagram 1
Marvin AI Platform: Architecture Views (2 of 4)

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Components
Diagram 2
Marvin AI Platform: Architecture Views (3 of 4)

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Deployment
Diagram (draft)
Marvin AI Platform: Architecture Views (4 of 4)

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Marvin AI - An open source platform to deploy and manage machine learning models
Hands On…

27. Fork me on Github.com/marvin-ai
and fell free to contribute.
Thank you!
twitter.com/_marvin_ai
gitter.im/marvin-ai