Title of the Presentation Goes Here © 2018 Carnegie Mellon University
QCon 2018 SÃO PAULO, BRASIL
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Marvin AI - An Open Source Platform to Deploy and Manage Machine Learning ModelsDaniel Takabayashi
QCON 2018
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About me…
Daniel Takabayashi • Technology Manager and Software Architect @ B2W Digital (São Paulo - Brazil) • Startups Mentor @ Founder Institute (San Francisco - USA) • Startups Mentor @ Aceleradora Cotidiano (Brasilia - Brazil) • Co-Founder of Boolabs, an Brazilian artificial intelligence startup acquired by B2W in 2016 • MSc in Computer Engineering (IPT - Brazil)
and my contacts… [email protected] @DanTakabayashi linkedin.com/danieltakabayashi github.com/takabayashi
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B2W Digital is the leading e-commerce company in Latin America.
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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…
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Some Problems in ML Projects
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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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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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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)
How to simplify the process of exploring, building, testing and
deploying machine learning projects in a reproducible way?
ABSTRACTION +
STANDARDIZATION
github.com/marvin-ai
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Marvin AI Platform
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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 • Meetup Group created in 01/2018 - https://www.meetup.com/pt-BR/
marvin-ai/ • Community is growing…
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Marvin AI Platform: Quality Attributes
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: • Modifiability - 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 TacticsQuality Attributes Main Tactics
InteroperabilityUsing gRPC connections between the EngineExecutor and the UserCode code and a DSL to describe the interfaces.
UsabilityCLI’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).
ManageabilityA Manager actor to control (locally or remotely) everything in the system and a cluster concept to help in distributed installations.
ScalabilityActor model architecture to increase parallelism and distribution throughout the system. Containerisation as deployment solution.
ModifiabilityEncapsulation through Actor model and base classes, minimum responsibility of each actor and a lot of abstraction.
MaintainabilityScala 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)
Hands On…
Fork me on Github.com/marvin-ai and fell free to contribute.
Thank you!
twitter.com/_marvin_aigitter.im/marvin-ai
