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5 elements of AI
Industry standard and open source platform
www.5elementsofai.com
About me
Tobias Oberrauch
Senior Consultant for CGI
E-Mail: tobias.oberrauch@pioneers.ai
• AI, Azure and new technologies like vue.js
• Guitar & Piano
• AI for good -> AI PIONEERS -> 5 elements of AI
„5 elements of AI“
Open
Industry standard
Open industry standard for a
structured and collaborative
execution of AI projects
Founded by many members of
the “KI Bundesverband” as well
as employees of CGI, Daimler
and Bosch.
Open source
Plattform
A platform that supports the
planning and implementation of AI
projects.
The platform can be operated
directly via the UI or used via the API.
Both can be installed on own
servers.
Community &
Meetups
A community that focuses on
AI.
A Meetup series that explains
the open industry standard
and open source platform.
Data & use cases
Development of new business models based on
existing use cases and identified opportunities
Use cases & data
Use case catalog
Use cases can consist of several algorithms and
are stored with the following information:
Input parameters, output parameters,
categories, description, effort, potential
www.appliedai.com
Examples
Data catalog
Data is stored with the following information:
Metadata (machine-processable), finding data
sources, controlling access to data, history of
data and data sources, origin of data and data
sources
Examples
• Cloudera Navigator, Datum, IBM Data
Governance, Hortonworks data steward
studio, Talend
• Cloud: Google, Azure, AWS and Watson
Knowledge Catalog
Priorize use cases
Use cases are evaluated and sorted according
to their value and simplicity
Simplicity
• Time
• Implementation speed
• Complexity
• Resources
Value
• Return on investment
Simplicity
Value
4
3
2
1
5
Strategy
The strategy is the central element and is constantly adapted
to the requirements and the environment.
Components
Data
Data catalogs
Use cases
Data & Use cases
Purpose
Requirements
Team
Projects
centre of excellence
Training and further education
Events
Monitoring and logging
Continuous improvement
Team
Onboarding
Center of excellence – Aufbau
Hosting & Deployment
Implementation & Scaling
Vision & Missions
Target groups
Competitor
Research
Core
Example
Requirements from customer
Train model
Provide modelPersist dataCollect data
4
5
6
Process data
6 6
1
3
2
1. GPU Infrastructure
2. Edge devices
3. Development platform for model development
4. Sensor technology for data acquisition
5. Device management
6. Data procurement
An environment in which AI cases are identified,
evaluated and provided.
13
Lighthouse projects
Lighthouse projects focus on rapid implementation and the creation of a
positive culture.
Leuchtturm-Projekte
Prototype
Implementation of an use case in an agile way. Try to proceed in
small steps with an try-and-error approach.
Purpose
The lighthouse project should have an effect on following
projects by serving inspiration and orientation.
Requirements
Agile approach, small team of qualified and creative employees,
few dependencies
Implementation & Scaling
This element ensures that the experience
from the lighthouse project is expanded.
Implementation & Scaling
From Protype to executable version
The system landscape is built around the Prototype: Frontend,
Backend, User Management…
Hosting & Deployment
Cloud, On Premise or hybrid architecture
Onboarding
A process that every team member has to go through to be
prepared for AI projects.
Center of Excellence – Structure
The Centre of Excellence defines the standards and procedures
in the strategy
Continuous improvement
This element ensures that AI projects
are constantly adapted to the market.
Continuous improvement
Center of Excellence
Centralized, dedicated AI team or distributed data scientists in each
team
Education and training
Development of a continuous knowledge transfer and access to
educational platforms such as Coursera, Udemy or edX
Events
Hackathons and other lighthouse projects.
Monitoring and logging
Continuous measurement of the performance of the implemented AI
algorithms. Regular improvement and correction of these algorithms.
Open Source
Platform
Technologies
• Frontend: vue.js 
• Backend: Python + ludwig.ai
• Databases: mongodb,
• DevOps: Docker, kubernetes
• User management: Keycloak
• Project management: JIRA
• Knowledge: Confluence
• Communication: Slack
• Source code: Gitlab
Roadmap
5 elements of AI
01.04.
2020
Open Source Platform: Start development
06.01.
2020
Open Standard 1.0
01.06.
2020
Open Source Platform Beta
Use cases
01.06.
2020
Open Standard 1.1
01.09.
2020
Open Source Plattform 1.0
Contact me and join 
Tobias Oberrauch
Senior Consultant for CGI
• E-Mail: tobias.oberrauch@pioneers.ai
• Phone: +49 151 174 256 23
• https://www.linkedin.com/in/tobiasoberrauch/
• https://www.xing.com/profile/Tobias_Oberrauch
• https://www.meetup.com/de-DE/aipioneers/members
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What is 5 elements of AI

  • 1. 5 elements of AI Industry standard and open source platform www.5elementsofai.com
  • 2. About me Tobias Oberrauch Senior Consultant for CGI E-Mail: tobias.oberrauch@pioneers.ai • AI, Azure and new technologies like vue.js • Guitar & Piano • AI for good -> AI PIONEERS -> 5 elements of AI
  • 3.
  • 4. „5 elements of AI“ Open Industry standard Open industry standard for a structured and collaborative execution of AI projects Founded by many members of the “KI Bundesverband” as well as employees of CGI, Daimler and Bosch. Open source Plattform A platform that supports the planning and implementation of AI projects. The platform can be operated directly via the UI or used via the API. Both can be installed on own servers. Community & Meetups A community that focuses on AI. A Meetup series that explains the open industry standard and open source platform.
  • 5.
  • 6. Data & use cases Development of new business models based on existing use cases and identified opportunities
  • 7. Use cases & data Use case catalog Use cases can consist of several algorithms and are stored with the following information: Input parameters, output parameters, categories, description, effort, potential www.appliedai.com Examples Data catalog Data is stored with the following information: Metadata (machine-processable), finding data sources, controlling access to data, history of data and data sources, origin of data and data sources Examples • Cloudera Navigator, Datum, IBM Data Governance, Hortonworks data steward studio, Talend • Cloud: Google, Azure, AWS and Watson Knowledge Catalog
  • 8. Priorize use cases Use cases are evaluated and sorted according to their value and simplicity Simplicity • Time • Implementation speed • Complexity • Resources Value • Return on investment Simplicity Value 4 3 2 1 5
  • 9. Strategy The strategy is the central element and is constantly adapted to the requirements and the environment.
  • 10. Components Data Data catalogs Use cases Data & Use cases Purpose Requirements Team Projects centre of excellence Training and further education Events Monitoring and logging Continuous improvement Team Onboarding Center of excellence – Aufbau Hosting & Deployment Implementation & Scaling Vision & Missions Target groups Competitor Research Core
  • 12. Train model Provide modelPersist dataCollect data 4 5 6 Process data 6 6 1 3 2 1. GPU Infrastructure 2. Edge devices 3. Development platform for model development 4. Sensor technology for data acquisition 5. Device management 6. Data procurement
  • 13. An environment in which AI cases are identified, evaluated and provided. 13
  • 14. Lighthouse projects Lighthouse projects focus on rapid implementation and the creation of a positive culture.
  • 15. Leuchtturm-Projekte Prototype Implementation of an use case in an agile way. Try to proceed in small steps with an try-and-error approach. Purpose The lighthouse project should have an effect on following projects by serving inspiration and orientation. Requirements Agile approach, small team of qualified and creative employees, few dependencies
  • 16. Implementation & Scaling This element ensures that the experience from the lighthouse project is expanded.
  • 17. Implementation & Scaling From Protype to executable version The system landscape is built around the Prototype: Frontend, Backend, User Management… Hosting & Deployment Cloud, On Premise or hybrid architecture Onboarding A process that every team member has to go through to be prepared for AI projects. Center of Excellence – Structure The Centre of Excellence defines the standards and procedures in the strategy
  • 18. Continuous improvement This element ensures that AI projects are constantly adapted to the market.
  • 19. Continuous improvement Center of Excellence Centralized, dedicated AI team or distributed data scientists in each team Education and training Development of a continuous knowledge transfer and access to educational platforms such as Coursera, Udemy or edX Events Hackathons and other lighthouse projects. Monitoring and logging Continuous measurement of the performance of the implemented AI algorithms. Regular improvement and correction of these algorithms.
  • 21. Technologies • Frontend: vue.js  • Backend: Python + ludwig.ai • Databases: mongodb, • DevOps: Docker, kubernetes • User management: Keycloak • Project management: JIRA • Knowledge: Confluence • Communication: Slack • Source code: Gitlab
  • 22. Roadmap 5 elements of AI 01.04. 2020 Open Source Platform: Start development 06.01. 2020 Open Standard 1.0 01.06. 2020 Open Source Platform Beta Use cases 01.06. 2020 Open Standard 1.1 01.09. 2020 Open Source Plattform 1.0
  • 23. Contact me and join  Tobias Oberrauch Senior Consultant for CGI • E-Mail: tobias.oberrauch@pioneers.ai • Phone: +49 151 174 256 23 • https://www.linkedin.com/in/tobiasoberrauch/ • https://www.xing.com/profile/Tobias_Oberrauch • https://www.meetup.com/de-DE/aipioneers/members